Saravanan666/pypi-org huggingface dataset:
Package: ganglion-biosensing
ganglion-biosensingModern Python 3.7+ library for interfacing with the OpenBCI Ganglion biosensing board over BTLE.The origins of this library come from my personal disagreement with some of the design and implementation choices made by the developers of the officialpyOpenBCI library. Some of the code in this repository has been obtained and/or adapted from the official pyOpenBCI code (following the MIT License).It should be noted that this library does not aim to be a full replacement for the official library, as it is completely focused on the Ganglion board and not on the Cyton. It should rather be looked at as an alternative which aims to target a more modern and efficient implementation from the ground up.InstallationSimply do:pipinstallganglion-biosensingAlternatively, install from the Github repository:pipinstallgit+https://github.com/molguin92/ganglion-biosensing.gitUsageUsage is pretty straightforward - simply declare a Ganglion within a with-block for automatic connection and cleanup:fromganglion_biosensing.board.ganglionimportGanglionBoardif__name__=='__main__':withGanglionBoard(mac='FF:FF:FF:FF:FF:FF')asboard:board.start_streaming()foriinrange(500):print(board.samples.get(block=True))The code is thread safe by design - samples are collected in an asynchronous manner and deposited in theboard.samplesqueue.For more details see theexamples/directory and the code itself.LicenseCopyright 2019 - Manuel Olguín Muñoz.Licensed under an MIT license, seeLICENSEfor details.
Package: ganify
GANifyDescription:GANify is an algorithm based on Generative Adversarial Learning to generate synthetic non-tensor data. The name GANify is an adaptation of acronymGAN(generative adversarial network) and AmplIFY, meaning you can amplify the amount of data available with GANs .Installation:One can easily install GANify using the PIP:pip install ganifyHow to use:Once installed, simply import the library and instatiate the model as described below:Other info:The package also enables the creation of synthetic data using bothGANsandWGANs, by simply changing the argument"type"on"fit_data"Additionally, you can view the model overall loss performance by calling the"plot_performance()"method after fit.
Package: ganit
GanitAn open source Multi-Platform Multi-Language support Programming Language/Library for Calculation Utility. The implementation is planned to be usingSplit and Mergealgorithm.The PlanThis is a project which is in planning phase and all the required publishing channels have been created. The main moto of this project is to create a Programming Language. The language will also be ported to many languages as library. This will facilitate any developer to work with mathematical expressions. This is lightweight and will have a limilted set of features as compared to Matlab, Wolfram, GNU Octave etc.The language is planned to be ported to the following languages as libraries -dotnetJavaScriptpythonJavaRustCShell ScriptdosCreditsIcons made bySmashiconsfromwww.flaticon.comis licensed byCC 3.0 BY
Package: ganite
GANITE - Estimation of individualized treatment effects using generative adversarial netsCode Author: Jinsung Yoon (jsyoon0823@g.ucla.edu)Paper: Jinsung Yoon, James Jordon, Mihaela van der Schaar, "GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets",
International Conference on Learning Representations (ICLR), 2018.DescriptionEstimating individualized treatment effects (ITE) is a challenging task due to the need for an individual’s potential outcomes to be learned from biased data and
without having access to the counterfactuals. We propose a novel method for inferring ITE based on the Generative Adversarial Nets (GANs) framework. Our method, termed Generative Adversarial Nets for inference of Individualized Treatment Effects (GANITE), is motivated by the possibility that we can capture the
uncertainty in the counterfactual distributions by attempting to learn them using a GAN. We generate proxies of the counterfactual outcomes using a counterfactual
generator, G, and then pass these proxies to an ITE generator, I, in order to train it. By modeling both of these using the GAN framework, we are able to infer
based on the factual data, while still accounting for the unseen counterfactuals. We test our method on three real-world datasets (with both binary and multiple
treatments) and show that GANITE outperforms state-of-the-art methods.Installation$pipinstallganiteExample UsagefromganiteimportGanitefromganite.datasetsimportloadfromganite.utils.metricsimportsqrt_PEHE_with_diffX_train,W_train,Y_train,Y_train_full,X_test,Y_test=load("twins")model=Ganite(X_train,W_train,Y_train,num_iterations=500)pred=model(X_test).numpy()pehe=sqrt_PEHE_with_diff(Y_test,pred)print(f"PEHE score for GANITE on{dataset}={pehe}")
Package: ganna-v1
Generative Algorithm for N-Doped Novel AromaticsThis algorithm can be used to generate nitrogen-doped polycyclic aromatic compounds whose geometry is optimised using the MMFF94s force field. The application of this algorithm is very straightforward to understand. The users are required to create a folder with the aromatic structures that they want to be doped with nitrogen atoms, specify the number of nitrogen atoms that they want in the structure, and provide the folder on which the MMFF94s optimised structures should be saved.  Please contact the author via email with any questions or concerns about the code (stiv.llenga@h-its.org).Setting the input variables:The idea behind this code is to keep it simple so that anyone working in the field of doped PAH compounds can use it to generate their compounds in a combinatorial fashion. Users are required to provide the following information:INPUT:--inp_dir -> (Str) The directory that houses all of the PAH xyz files;--out_dir -> (Str) The directory where the N-doped PAH must be saved;--hetero_nr -> (Int) The total amount of dopants (Nitrogen atoms);--iters -> (Int) The maximum iteration value of the force field.
Package: gannswing
gann-swingPython module to calculate Gann swingsWhy?I've gotten sick of waiting for someone to write a robust, correct Python module to calculate Gann swings with all the variations and optional parameters I want, so thought I may as well take it on myselfTo run all test cases & confirm everything is working OK$ pytest -vTo use it(add in later)Consuming data from different data sourcesGannSwing is expecting data to be provided in a Pandas dataframe with the following set of column headings:TimestampOpenHighLowCloseAll these columns are mandatory; any other columns in the dataframe are ignored.OptumaOptuma lets you copy/paste data in a CSV, tab-limited format. Here's an example of how to consume this data format$ ipython
> from gannswing import GannSwing
> from pandas import pd
> bars = pd.read_csv('./tests/data/optuma.csv', delimiter='\t', usecols=['Date', 'Open', 'High', 'Low', 'Close'])
> bars.rename(columns={'Date': 'Timestamp'}, inplace=True)
> gs = GannSwing(bars=bars)ProfitSource(add later)Yahoo Finance(add later)To-do listQuite a long list, but it gives me a task list to prioritise and work to:calculate ticksizework out the structure of the Pandas dataframe to deliver resultspackage it into a proper Python module that I can install usingpip install gann-swingsimplement support for 1-bar, 2-bar, 3-bar, ... swingsbe able to draw a quick chart of swings overlaid on top of a bar chart as a visual reference pointbuild a huge test suite that covers all the functionality. Tests should be able to confirm that it e.g. calculates swings correctly when it gets 17 outside days in a row...work out the trend as well as the swingscalculate the days and price difference between successive swingsoption to create a swing for inside days in a down trendoption to filter out tiny swingsoption to compare the open & close on outside days to determine whether where to put a swing in the daily chart without waiting for the next up or down daybuild in support for consuming OHLC data from a range of common data sources
Package: ganntchart
Installation$ pip install git+https://github.com/EdwardHong0627/gannt_chartImportingfrom ganntchart.ganntchart import GanntChartUsageFirst of all, we need to initialize the blank gannt chart
with specified number of machine, total length of gannt chart, and the sale size.i.e.,result =GanntChart(num_of_machine=2, 
                   total_length=50, 
                   scale=20)The statement above means that result is an instance of object Gannt Chart.Second we can call the add_job function to append a job information:result.add_job(job_name='J_1', 
               machine=0, 
               starting_time=0, 
               processing_time=5)\you can also assign the fill-color and stroke-color of jobs in Gannt chart by adding arg in the add job function as below:result.add_job(job_name='J_1', 
               machine=0, 
               starting_time=0, 
               processing_time=5,
               fill= '#00FF00',
               stroke= '#FF00FF')\Finally, we can call save_to_file function to save our Gannt chart as a svg_fileresult.save_to_file(file_name='result')
Package: ganpdfs
GANPDFsEnhance the statistics of a prior PDF set by generating fake PDF replicas using Generative
Adversarial Neural Networks (GANs). Documentation
is available athttps://n3pdf.github.io/ganpdfs/.How to installTo install theganpdfspackage, just typepythonsetup.pyinstallorpythonsetup.pydevelop(ifyouareadevelopper)The package can be installed via the Python Package Index (PyPI) by running:pipinstallganpdfs--upgradeHow to runThe code requires as an input aruncard.ymlfile in which the name of the PDF set and the
characteristics of the Neural Network Models are defined. Examples of runcards can be found
in theruncardfolder.ganpdfsruncard/reference.yml[-tTOT_REPLICAS_SIZE]In case one does not want to train the GANs and directly resort to a pre-trained one, a pre-trainedmodelcan be used out of the box by setting the entryuse_saved_modeltoTruein the runcard.In order to evolve the generated output grids, just run:evolven3fit<PRIOR_PDF_NAME>_enhanced<TOT_REPLICAS_SIZE>Then, to link the generated PDF set to the LHAPDF data directory, use thepostgansscript by
running:postgans--pdf<PRIOR_PDF_NAME>--nenhanced<TOT_REPLICAS_SIZE>Hyper-parameter opitmizationFor more details on how to define specific parameters when running the code and on how to perform
a hyper-parameter scan, please head to the sectionhow toof the documentation.
Package: gan-recover-image
MIT LicenseCopyright (c) 2020 Fujinet System SJCPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: gans-implementations
GANs-ImplementationsGANs Implementations and other generative models + Training (in ./notebooks)Implemented:Vanilla GANDCGAN - Deep Convolutional GANWGAN - Wasserstein GANSNGAN - Spectrally Normalized GANSRGAN - Super Resolution GANStyleGANPix2PixHDC-VAE - Convolutional Variational Auto-encoderInstallationPyPi Installation$pipinstallgans-implementationsLocal Install and Run:$cd{PROJECT_DIRECTORY}$pipinstall-e.ExampleIn notebooks directory there is a notebook on how to use each of these models for their intented use case; such as image
generation for StyleGAN and others. Check them out!fromgans_package.modelsimportStyleGAN_Generator,StyleGAN_Discriminatorin_channels=256out_channels=3hidden_channels=512z_dim=128mapping_hidden_size=256w_dim=512synthesis_layers=5kernel_size=3in_size=3d_hidden_size=16g=StyleGAN_Generator(in_channels,out_channels,hidden_channels,z_dim,mapping_hidden_size,w_dim,synthesis_layers,kernel_size,device=DEVICE).to(DEVICE)d=StyleGAN_Discriminator(in_size,d_hidden_size).to(DEVICE)importtorchnoise=torch.randn(BATCH_SIZE,z_dim).to(DEVICE)fake=g(noise)pred=d(fake)Handwritten Digits - MNISTGAN with BCELossDCGAN with BCELossSN-WGAN with Wasserstein LossWork Citedhttps://arxiv.org/pdf/1609.04802v5.pdfhttps://arxiv.org/pdf/1812.04948.pdfhttps://www.coursera.org/specializations/generative-adversarial-networks-gans?
Package: ganslate
ganslate- a simple and extensible GAN image-to-image translation frameworkFor comprehensive documentation, visit:https://ganslate.readthedocs.io/en/latest/Note: The documentation is still in progress! Suggestions and contributions are welcome!ganslateis aPyTorchframework which aims to make GAN image-to-image translation more accessible to both beginner and advanced project with:Simple configuration systemExtensibility for other datasets or architecturesDocumentation andvideo walk-throughs (soon)Features2D and 3D supportMixed precisionDistributed trainingTensorboard andWeights&BiasesloggingNatural and medical image supportA range of generator and discriminator architecturesAvailable GANsPix2Pix (paper)CycleGAN (paper)RevGAN (paper)CUT (Contrastive Unpaired Translation) (paper)Projectsganslatewas used in:Project 1Project 2CitationIf you usedganslatein your project, please cite:@software{ibrahim_hadzic_2021_5494572,
  author       = {Ibrahim Hadzic and
                  Suraj Pai and
                  Chinmay Rao and
                  Jonas Teuwen},
  title        = {ganslate-team/ganslate: Initial public release},
  month        = sep,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v0.1.0},
  doi          = {10.5281/zenodo.5494572},
  url          = {https://doi.org/10.5281/zenodo.5494572}
}
Package: ganttchart
gantt chart ,  a class for showing a gantt chart quickly !chart = ganttChart()
chart.addTask("User", 1234567.11334, 1234600.11334, "Step1/resource1", "orange")
chart.addTask("User", 1234600.11334, 1234700.11334, "Step2/resource2", "green")
chart.show()
Package: ganttify
ganttifyThis is a thin wrapper around the Altair python visualization library
specifically for creating Gantt charts of CI pipeline results given data in
the following JSON format:[
    {
        'queue': 'OPTIONAL - pandas parsable timestamp',
        'start': 'REQUIRED - pandas parsable timestamp',
        'end': 'REQUIRED - pandas parsable timestamp',
        'status': 'REQUIRED - any string other than "success" is interpreted as a failure',
        'label': 'REQUIRED - label string',
    },
    ...
]This satisfies my own use cases, use at your own risk.For more control, recommend importing the ganttify module and invoking
make_chart from a script.
"""
Package: gantt-lib-lautarodapin
python gantt libThis project is an extension of thepython-ganttmodule, providing some extra functionality.
This includes some translation to spanish of the days and months, new classes for add hyperlinks to the tasks and projects, and svg in format string for using it as context in Django lib.InstalationRun the following to install:pip install gantt-lib-lautarodapinAditional requirementsLibrary:python-ganttseelinkAuthor: Alexandre Norman (norman at xael.org)Usageimportgantt_libimportdatetimeresource=gantt_lib.Resource("Resource 1")project=gantt_lib.HiperLinkedProject(name='Project',link='mypage/link/to/project',target='_self'#HTML target (it opens the link on the same page))project.add_task(gantt_lib.HyperLinkedTask(name='task 1',start=datetime.datetime.date(2020,7,1),duration=5,resources=[resource],percent_done=0,color="#03d529",link_name='mypage/link/to/title',link_resource='mypage/link/to/resource',link_lateral='mypage/link/to/extra',target='_self'#HTML target (it opens the link on the same page)))# make_svg_for_task returns the xml code in string format, this way you can use it as context in django or flask# also saves it into a filesvg=project.make_svg_for_tasks('file.svg',today=datetime.date(2020,7,15),start=datetime.date(2020,7,1),end=datetime.date(2020,7,20))# Or you can get only the svg (str) codesvg=project.get_string_svg_for_tasks(today=datetime.date(2020,7,15),start=datetime.date(2020,7,1),end=datetime.date(2020,7,20))Result:SVGAuthorLautaro DapinoVersion 0.0.5Addedtargetfor the links.Added new method for rendering thesvgintostrformat.
Package: ganutils
Gan's UtilsThis is an installable python package for scripts I wrote for myself.PyPi Package PagelinkDocumentationlinkInstallationTo install, simply run$ pip install ganutilsTo use the AI package, you will need to installPyTorchyourself.DeploymentTo deploy new versions to PyPi, change version number insetup.pyand eithercreate a new release for repo and GitHub workflow will automatically deploy itrunpython setup.py sdistand thentwine upload dist/*.Update DocumentationCurrently, upon each new release, my GitHub action will automatically build and generate documentation necessary.However, to manually generate documentation, I usepdoc3:$ pip3 install pdoc3 # install dependency
$ pdoc3 --html ganutils # resulting docs will be in a new html/ folderDependenciesYou'll needtwineinstalled viapipand PyPi credentials set at$HOME/.pypirc
Package: ganymede
Have you also wondered where the fourth Galilean moon inJupyter’s logo has gone? Well, it is told that
after many centuries of Ganymede’s absence from Mount Olympus, Zeus felt
a deep and painful longing. So he sent out his eagle again to find
Ganymede and bring him back. But Ganymede always wanted to be more than
just a servant for the pleasure of the gods. He turned to Jupyter with
an offer. If Jupyter grant him shelter in his giant atmosphere then
Ganymede would build him a new temple. A temple to attract a whole new
generation of worshippers. Jupyter accepted…Currently, the temple can only be visited from Python. There will soon
be other ways to get there. Just usepipto automatically install the latestgatewith its dependencies
fromPyPI:pip install ganymedeIt can also be visited in development mode, directly from Ganymede’s
repository. The additional development dependencies are automatically
resolved by running the following commands in the repository’s root
directory:pip install -r requirements.txt
pip install -r requirements.dev.txt
pip install -e .You also need an installedCoffeeScriptcompiler for development mode.Then open an IPython notebook in Jupyter’s web interface and:Some of Jupyter’s control elements are hidden, like the cell format and
cell toolbar select boxes. They still need to find their new place,
along with these additional features coming soon:Menu button in slim mode tool barRedocking of cell outputs to the consoleVisual indicators for cell inputs on hovering their undocked outputs
and vice versa and auto console scrolling to inputSwitchable virtual background screens for undocked outputs with
selection bar in menu areaA %terminal magic to embed Jupyter’s remote terminals in notebooksTabbed multi-notebook console and ability to mix undocked cell
outputs from different notebooks and kernels
Package: ganymede-logo
ganymede-logoSet a Systemathics logo for ganymedeRequirementsJupyterLab >= 3.0Installpipinstallganymede-logoContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the ganymede-logo directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmrunbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmrunwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm run buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseUninstallpipuninstallganymede-logo
jupyterlabextensionuninstallganymede-logo
Package: ganzo
GanzoCreates new project from templates stored in git repositories.How to use?Ganzo assumes the existence of a folder$HOME/.ganzocontaining the configuration./$HOME
    /.ganzo
        configuration.jsonExampleconfiguration.json:{
    "gcs_bucket_name": "<gcs_bucket_name>" // Google Cloud Storage bucket where the template list is stored.
}Then check how to use ganzo in from commandline.$ ganzo -hTemplatesTemplates are store in a directory containing atemplates.listfile which contains a template entry per line.Thetemplates.listfile for the previous directory should list all the templates available.# Content of templates.list
<template_name> <template_git_url> <template_git_branch>
app git@github.com:organozation/project_app.git main
list https://github.com/organozation/project_list.git devVariable resolutionFiles with extension.nzoare candidates for variable resolution,
which will replace variables within the files with project specific values.# Content of README.md.nzo

This projects name is ${PROJECT_NAME}.Currently the only available variable for resolution isPROJECT_NAME.Legacy Templates (version <= 0.5.0)Templates are store in a directory containing atemplates.listfile,
which contains a template name per line./path/to/templates
    templates.list
    /app
        /code
        pyproject.toml
        ...
    /lib
        /scripts
        README.md.nzo
        ...Thetemplates.listfile for the previous directory should list all the templates available.# Content of templates.list
<template_name>
app
listEach templates directory will be fully copied as part of loading the template for a new project.DevelopmentPython virtual environmentCreate and load a virtual environement.python -m venv .venv
source .venv/bin/activateInstallInstall all dependencies in editable mode.make installMiscCheck what other shortcuts are available.make helpAreas of improvementSupport for custom variables.Directory structure changes based on variables.Improve coverage of core and resolvers.Improve error handling and messages
Package: ganzoo
# GAN ZOO[![Documentation](https://img.shields.io/badge/documentation-master-green.svg)](https://innerlee.github.io/ganzoo/)
[![PyPI Version](https://img.shields.io/pypi/v/ganzoo.svg)](https://pypi.python.org/pypi/ganzoo)
[![Python Version](https://img.shields.io/pypi/pyversions/ganzoo.svg)]()
[![Build Status](https://travis-ci.org/innerlee/ganzoo.svg?branch=master)](https://travis-ci.org/innerlee/ganzoo)
[![Coverage Status](https://codecov.io/gh/innerlee/ganzoo/branch/master/graph/badge.svg)](https://codecov.io/gh/innerlee/ganzoo)ganzooimplements all the GANs. Currently, it includesGANDCGANWGANWGAN-GPCGANTry and start with`shell pip install ganzoo `
Package: gaode
Gaode - 高德 APIGaode SDK for Python, which based onhttps://lbs.amap.com/api/webservice/summary/InstallpipinstallgaodeUsageAll API supported! please visitSupported methodsfor more information!fromgaodeimportGaodetoken="TOKEN"word="饸饹"gd=Gaode(token)ret=gd.place_text(word=word)# Limited to use `keyword arguments`{"count":"0","info":"OK","infocode":"10000","pois":[],"status":"1","suggestion":{"cities":[{"name":"西安市","num":"1849"},...],"keywords":[]}}