Saravanan666/pypi-org huggingface dataset:
Package: galpynostatic
galpynostaticgalpynostaticis a Python package with physics-based models to predict
optimal conditions for fast-charging lithium-ion batteries.RequirementsYou need Python 3.9+ to run galpynostatic. All other dependencies, which are the
usual ones of the scientific computing stack
(matplotlib,NumPy,pandas,scikit-learnandSciPy), are installed automatically.InstallationYou can install the latest stable release of galpynostatic withpippython -m pip install --upgrade pip
python -m pip install --upgrade galpynostaticUsageTo learn how to use galpynostatic you can start by following thetutorialsand then read theAPI.You can also read the Jupyter Notebook pipelines in thepapers folderto reproduce the results of the published articles.Licensegalpynostatic is licensed under theMIT License.CitationsIf you use galpynostatic in a scientific publication, we would appreciate it if
you could cite the following article:F. Fernandez, E. M. Gavilán-Arriazu, D. E. Barraco, A. Visintin, Y. Ein-Eli and
E. P. M. Leiva. "Towards a fast-charging of LIBs electrode materials: a
heuristic model based on galvanostatic simulations."Electrochimica Acta 464(2023): 142951.For the theoretical framework and the universal map datasets
(galpynostatic.datasetssubmodule) refer to:E. M. Gavilán‐Arriazu, D. E. Barraco, Y. Ein‐Eli and E. P. M. Leiva.
"Galvanostatic Fast Charging of Alkali‐Ion Battery Materials at the
Single‐Particle Level: A Map‐Driven Diagnosis".ChemPhysChem, 24.6 (2023):
e202200665.BibTeX entries can be found in theCITATIONS.bibfile.ContactIf you have any questions, you can contact me atffernandev@gmail.com
Package: galsampler
GalSamplerTools for generating synthetic cosmological data.InstallationThe latest release of galsampler, v0.1.0, can be installed with conda-forge:$ conda install lsstdesc-galsamplerTo install galsampler into your environment from the source code:$ cd /path/to/root/galsampler
$ pip install .However you install the code, to use it from a python interpreter:>>> import galsamplerDocumentationSeehttps://galsampler.readthedocs.io/en/latestfor complete documentation and usage tutorials.Citation informationThe galsampler paper has been published in MNRAS. Citation information for the paper can be found at [this ADS link](https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.5040H/exportcitation), copied below for convenience:@ARTICLE{2020MNRAS.495.5040H,
      author = {{Hearin}, Andrew and {Korytov}, Danila and {Kovacs}, Eve and {Benson}, Andrew and {Aung}, Han and {Bradshaw}, Christopher and {Campbell}, Duncan and {LSST Dark Energy Science Collaboration}},
      title = "{Generating synthetic cosmological data with GalSampler}",
      journal = {\mnras},
keywords = {large-scale structure of Universe, Astrophysics - Cosmology and Nongalactic Astrophysics},
      year = 2020,
      month = jul,
      volume = {495},
      number = {4},
      pages = {5040-5051},
      doi = {10.1093/mnras/staa1495},
archivePrefix = {arXiv},
      eprint = {1909.07340},
primaryClass = {astro-ph.CO},
      adsurl = {https://ui.adsabs.harvard.edu/abs/2020MNRAS.495.5040H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Package: galsen-game-of-life
Jeu de la vie ConwayDescriptionLe jeu de la vie (ou game of life) est un automate cellulaire évoluant sur une grille (de taille théoriquement infinie)
composée de cases carrées appelées cellules qui ont un état binaire (1 pour vivante et 0 pour morte). Les cellules
évoluent dans le temps en fonction de leur voisinage (chaque cellule a 8 cellules voisines)., ce qui modifie la grille
à chaque étape d'évolution (appelée génération).RèglesUne cellule morte possédant exactement 3 voisines vivantes devient vivante (elle naît).Une cellule vivante possédant 2 ou 3 voisines vivantes le reste, sinon elle meurt.Les autres cellules (mortes ou vivantes) restent dans leur état actuel.ImplémentationLe jeu de la vie est implémenté en python sous forme de package:game_of_lifeLe package est publie sur PyPi:https://pypi.org/project/galsen-game-of-life/UtilisationOn commence par installer le package:pip3installgalsen-game-of-lifePour tester notre implementation du jeu de la vie on a plusieurs options:1. application client/serveurOn a mis en place unwebsocketclient/serveur.Pour lancer le serveur websocket, il faut lancer le scriptmain_server.pydans le dossierapi/:python3api/main_server.pyLe serveur websocket est alors accessible surws://localhost:8000Ensuite on a mis en place un client javascript pour pouvoir communiquer avec le serveur websocket.
Il suffit juste d'ouvrir le fichierindex.htmldans un navigateur web pour pouvoir jouer au jeu de la vie.Spoiler Alert:On est pas trop bon en html/css par ici!!2. application consoleOn peut aussi jouer au jeu de la vie en ligne de commande. Pour cela il vous faudra un fichier de depart contenant un etat initial
Exemple de fichier de depart:0111101101000000000110001100100110110101000001110000010010101011100101110000001110101101111000001000Pour lancer le jeu de la vie en ligne de commande, il faut ensuite installer le packagegalsen-game-of-life:pip3installgalsen-game-of-lifeensuite on peut lancer en ligne de commande:galsen-game-of-life--inputtest-data.txt--num-steps10--output-filetest-data-out.txtSi on veut voir les etapes intermediaires, on peut ajouter l'option--verbose:galsen-game-of-life--inputtest-data.txt--num-steps10--output-filetest-data-out.txt--verbose1
Package: galsim-hub
GalSim HubGalSim Hub provides a collection of TensorFlow modules for GalSim.TL;DRGalSim Hub makes it easy to incorporate galaxy light profiles modelled by deep neural networks directly with a GalSim script:importgalsimimportgalsim_hubfromastropy.tableimportTable# Load a generative model from the online repositorymodel=galsim_hub.GenerativeGalaxyModel('hub:Lanusse2020')# Defines the input conditions, for this model flux_radius, mag_auto, and redshiftcatalog=Table([[5.,10.,20.],[24.,24.,24.],[0.5,0.5,0.5]],names=['flux_radius','mag_auto','zphot'])# Sample light profiles for these parametersprofiles=model.sample(catalog)...You can read more, and try out a live demo here:InstallationTo install:$pipinstall--usergalsim-hubNote: GalSim-Hub currently assumes TensorFlow 1.15 to be installed, and will not try to install it as a dependency.UsageThis module can be used directly under the GalSim Yaml driver:$galsimexamples/demo14.yamlor from Python:importgalsimimportgalsim_hubfromastropy.tableimportTable# Load a generative model from the online repositorymodel=galsim_hub.GenerativeGalaxyModel('hub:Lanusse2020')# Defines the input conditions, for this model flux_radius, mag_auto, and redshiftcatalog=Table([[5.,10.,20.],[24.,24.,24.],[0.5,0.5,0.5]],names=['flux_radius','mag_auto','zphot'])# Sample light profiles for these parametersprofiles=model.sample(catalog)...Adding a model to the repositoryThis repository is intended to host a collection of deep generative models, any new contributions are welcome.
Submitting your model to the Hub means that it becomes trivially accessible to any GalSim user, increasing the chances that it will be used in practice :-)To submit a model, please open a Pull Request adding a new folder under thehubdirectory. As the procedure is not currently completely documented, do not hesitate to reach out to the maintainers of the repository for guidance.
Package: galtab
galtabUse JAX-accelerated galaxy tabulation to compute HOD counts-in-cells statisticsInstall:pip install galtabRead the docs:https://galtab.readthedocs.orgTo citegaltab, learn more implementation details, and explore an example science use case, check outhttps://arxiv.org/abs/2309.08675.Source code:https://github.com/AlanPearl/galtabAuthorAlan PearlPrerequisitespython>=3.8mpi4pywith MPI backend (several implementations available - seehttps://pypi.org/project/mpi4py)Example to automatically install prerequisites using a conda environment:conda create -n py310 python=3.10
conda activate py310
conda install -c conda-forge mpi4pyOptional dependencies for thepaper2subpackagemocksurvey and pycorr (instructions athttps://github.com/AlanPearl/mocksurvey)tabcorr (pip install tabcorr)emcee (pip install emcee)nautilus (pip install nautilus-sampler)uncertainties (pip install uncertainties)
Package: galter-subjects-utils
galter-subjects-utilsSubjects tooling for InvenioRDM.When a new list of terms (e.g. MeSH) comes out, this tool can be used to update the list in a dedicated subject distribution package.
This tool is typically required by the dedicated subject distribution packages under the[dev]extra.The tool follows the same typical pattern for any subject:Download the new listRead it with optional filtersConvert vocabulary entries to InvenioRDM subjects formatWrite those to fileInstallationpipinstallgalter-subjects-utilsVersionsThis repository followssemantic versioningindexed on invenio-app-rdm compatibility according to the table below:This Versioninvenio-app-rdm version0.X11.X1.X11.XThis just means for example that version 1.X guarantees generation of subjects files compatible with invenio-app-rdm v11. When there is a break in subjects format, this tool will bump its major version.Usagegalter-subjects-utils--helpDevelopmentInstall the project in editable mode withdevdependencies in an isolated virtualenv ((venv)denotes that going forward):(venv)pipinstall-e.[dev]Run tests:(venv)invoketest# or shorter(venv)invtestCheck manifest:(venv)invcheck-manifest
Package: galumph
GALUMPHCalculate ALM (scattering amplitude decomposed into spherical harmonics) at Hyperspeed using GPU acceleration.This is a preliminary implementation using PyOpenCL.Example code:importnumpyasnpimportBio.PDBimportperiodictableimportpyopenclimportgalumphctx=pyopencl.create_some_context()NS=4096# Number of S values at which to calculate the scatteringsmax=1.0# Maximum S valueLMAX=63# Maximum harmonic order to use for the calculations## Initialise the S array and allocate the ALM storage on the GPUs=np.linspace(0,smax,NS)kernel=galumph.AtomicScattering(LMAX,s,ctx=ctx)## Use Bio.PDB to read the structure and periodictable to calculate the atomic form factorspdb=Bio.PDB.PDBParser().get_structure("6lyz","6lyz.pdb")xyz=np.array([aa.get_vector().get_array()foraainpdb.get_atoms()])ff=np.array([periodictable.elements.symbol(aa.element).xray.f0(s)foraainpdb.get_atoms()])## Run the GPU calculationalm=kernel.zeros()kernel.add_many_atoms(alm,xyz,ff)Icalc=kernel.sum_intensity(alm)
Package: galvani
galvaniRead proprietary file formats from electrochemical test stationsBio-Logic .mpr filesUse theMPRfileclass from BioLogic.py (exported in the main package)from galvani import BioLogic
import pandas as pd

mpr_file = BioLogic.MPRfile('test.mpr')
df = pd.DataFrame(mpr_file.data)Arbin .res filesUse the res2sqlite.py script to convert the .res file to a sqlite3 database
with the same schema.
Package: galytics3
Обертка над стандартной библиотекой google_api_python_client для легкой работы с API Google Analytics v3Написано на версии python 3.5Умеет запрашивать данные маленькими порциями,
чтобы обойти семплирование.
Также если в один ответ не поместятся все строки (макс 10000 строк),
сделает дополнительные запросы.Установкаpip install --upgrade galytics3Как пользоватьсяУказание авторизационных данных.Эта обертка не умеет получать токен, он у вас уже должен быть.
Как получить? Гуглите.Вариант 1fromgalytics3importGoogleAnalyticsapi=GoogleAnalytics(refresh_token='{refresh_token}',client_id='{client_id}',client_secret='{client_secret}')Вариант 2Если у вас объект credential создается другим образом.
Через файл или еще как-то.fromgalytics3importGoogleAnalyticscredentials=credentials_object# Ваш объект credentialapi=GoogleAnalytics(credentials=credentials)Вариант 3Объявление дополнительных настроек, типа кеширования.fromgoogleapiclient.discoveryimportbuildfromgalytics3importGoogleAnalyticscredentials=credentials_object# Ваш объект credential# В build можно объявить дополнительные настройки, вроде кеширования и т.д.service=build('analytics','v3',credentials=credentials_object)api=GoogleAnalytics(service=service)Получаем данныеfromdatetimeimportdatetimefromgalytics3importGoogleAnalyticsapi=GoogleAnalytics(refresh_token='{refresh_token}',client_id='{client_id}',client_secret='{client_secret}')# Получит все аккаунты, ресурсы и представления.df=api.get_accounts(as_dataframe=True)# По умолчанию данные возвращаются в формате dataframedata=api.get_accounts(as_dataframe=False)# Вернуть в JSONprint(df)print(data)# Получит все цели всех представлений.df=api.get_goals()print(df)# Запросить стандартный отчетdf=api.get_report(id=12345789,source='GA',date1=datetime(2019,1,1),date2=datetime(2019,1,10),dimensions=['ga:date'],metrics=['ga:percentNewSessions'],sort='ga:date')print(df)# Запросить отчет MCFdf=api.get_report(id=12345789,source='mcf',date1=datetime(2019,1,1),date2=datetime(2019,1,10),dimensions=['mcf:sourceMediumPath','mcf:conversionDate, mcf:source'],metrics=['mcf:totalConversions','mcf:totalConversionValue'],sort='mcf:source',filters='mcf:ConversionType==Transaction')print(df)ЗависимостиpandasАвторПавел МаксимовСвязаться со мной можно вТелеграми вFacebookУдачи тебе, друг! Поставь звездочку ;)
Package: gam
GAM (Global Attribution Mapping)Global Explanations for Deep Neural NetworksGAM explains the landscape of neural network predictions across subpopulations.This implementation is based on "Global Explanations for Neural Networks: Mapping the Landscape of Predictions" (AAAI/ACM AIES 2019).Installationpython3-mpipinstallgamGet StartedFirst generate local attributions using your favorite technique, then:>>>fromgam.gamimportGAM>>># for a quick example use `attributions_path="tests/test_attributes.csv"`>>># Input/Output: csv (columns: features, rows: local/global attribution)>>>gam=GAM(attributions_path="<path_to_your_attributes>.csv",distance="spearman",k=2)>>>gam.generate()>>>gam.explanations[[('height',.6),('weight',.3),('hair color',.1)],[('weight',.9),('weight',.05),('hair color',.05)]]>>>gam.subpopulation_sizes[90,10]>>>gam.subpopulations# global explanation assignment[0,1,0,0,...]>>>gam.plot()# bar chart of feature importance with subpopulation sizeTestsTo run tests:$python-mpytesttests/ContributorsWe welcome Your interest in Capital One’s Open Source Projects (the
“Project”). Any Contributor to the Project must accept and sign an
Agreement indicating agreement to the license terms below. Except for
the license granted in this Agreement to Capital One and to recipients
of software distributed by Capital One, You reserve all right, title,
and interest in and to Your Contributions; this Agreement does not
impact Your rights to use Your own Contributions for any other purpose.Sign the Individual AgreementSign the Corporate AgreementCode of ConductThis project adheres to theOpen Code of ConductBy participating, you are
expected to honor this code.
Package: gama-cli
GAMA CLIPublicly available onPyPifor convenience but if you don't work at Greenroom Robotics, you probably don't want to use this.InstallFor development:pip install -e ./libs/gama_configpip install -e ./tools/gama_cliFor production:pip install gama-cliYou may also need toexport PATH=$PATH:~/.local/binif you don't have~/.local/binin your pathInstall autocomplete:bash:echo 'eval "$(_GAMA_CLI_COMPLETE=bash_source gama)"' >> ~/.bashrczsh:echo 'eval "$(_GAMA_CLI_COMPLETE=zsh_source gama)"' >> ~/.zshrc(this is much nicer)Usagegama --helpto get help with the CLIGroundstationInstalling a GAMA on a groundstation is as simple as this:mkdir ~/gama && cd ~/gamagama authenticateto authenticate with the GAMA package registrygama gs configureto configure GAMA on a groundstationgama gs installto install GAMA on a groundstationgama gs upto start GAMA on a groundstationgama gs downto stop GAMA on a groundstationVesselInstalling a GAMA on a vessel is as simple as this:mkdir ~/gama && cd ~/gamagama authenticateto authenticate with the GAMA package registrygama vessel configureto configure GAMA on a vesselgama vessel installto install GAMA on a vesselgama vessel upto start GAMA on a vesselgama vessel downto stop GAMA on a vesselDev modeGAMA CLI can be ran in dev mode. This will happen if it is installed withpip install -e ./tools/gama_clior if the environment variableGAMA_CLI_DEV_MODEis set totrue.
Package: gama-config
GAMA ConfigGAMA Config is used to load config stored inside the.gamafolder.Installpip install -e ./libs/gama_configor...pip install gama_config(Public onPyPi)UsageReading configfromgama_config.gama_vesselimportread_vessel_configfromgama_config.gama_gsimportread_gs_configvessel_config=read_vessel_config()gs_config=read_gs_config()Writing configfromgama_config.gama_vesselimportwrite_vessel_config,GamaVesselConfigfromgama_config.gama_gsimportwrite_gs_config,GamaGsConfigvessel_config=GamaVesselConfig()gs_config=GamaGsConfig()write_vessel_config(vessel_config)write_gs_config(gs_config)Generating schemasAfter changing the dataclasses, you can generate the schemas with:python3-mgama_config.generate_schemasRunning testspython3-mpytest-v./libs/gama_config
Package: gamayun-utils
Python utilities for GamayunThis is a package containing python utilities for writing Gamayun jobs.
Main Gamayun repository can be foundhere.ContentsGetting the packageUsageReporting results and errorsExecuting the script logicGetting the packageInstall the package with the following command:pip3installgamayun-utilsThis package depends on gprcio, which needs C/C++ compilers installed on the system.
Needed dependencies on Alpine are:apkadd--no-cachepython3\&&apkadd--no-cachegcc\&&apkadd--no-cacheg++\&&apkadd--no-cachelibc-dev\&&apkadd--no-cachelinux-headers\&&apkadd--no-cachepython3-devNote that suggested way of using Gamayun is with Docker, so you don't have to install these dependencies locally. If you are using Docker, there is an image containing Gamayun and Gamayun Python utilitieshere.SeeGamayun main repofor more information and see example usagehere.UsageThis part documents the package APIReporting results and errorsFunctions for reporting results or errors from the script are following:defreport_result_with_strings_only(results)This function is used when the result we want to report is a list of stringsdefreport_result_with_maps_only(results)This function is used when the result we want to report is a list of mapsdefreport_result_with_maps_and_strings(mapResults,stringResults)This function is used when the result we want to report is a list of strings and a list of mapsdefreport_error(error)This function is used when we want to report error which will be forwarded through the notifier interfaceNote that once result or error is reported for the script/gamayun-job, Gamayun will stop listening for results/errors for that job, so in entire job there should be only one call to any of thereportfunctions.Executing the script logicThere is a function with the following signature in this package:defrun_gamayun_script_logic(callback)All the script logic should be placed in some function and that function should be given torun_gamayun_script_logicas argument as this will ensure that all uncought exceptions are caught and reported as errors to Gamayun.So, a job script file should look something like this:fromgamayun_utilsimportreport_result_with_maps_onlyfromgamayun_utilsimportreport_errorfromgamayun_utilsimportrun_gamayun_script_logicdefscript_logic():# place the script logic hereifOK:report_result_with_maps_only(results)else:report_error(error)run_gamayun_script_logic(script_logic)
Package: gamba
Welcome to the gamba library!A Python library for player behaviour tracking research---------------------------------------------------------------------------

ModuleNotFoundError                       Traceback (most recent call last)

/tmp/ipykernel_5080/442603745.py in <module>
      1 #export
----> 2 from gamba.data import *
      3 from gamba.measures import *
      4 from gamba.statistics import *
      5 from gamba.labelling import *


~/Desktop/gamba/gamba/__init__.py in <module>
      5 # Cell
      6 from .data import *
----> 7 from .measures import *
      8 from .statistics import *
      9 from .labelling import *


~/Desktop/gamba/gamba/measures.py in <module>
    618 
    619 # Cell
--> 620 from tqdm import tqdm
    621 import datetime, pandas as pd
    622 def calculate_labrie_measures(all_player_bets, savedir="", filename="gamba_labrie_measures.csv", loud=False, daily=True,):


ModuleNotFoundError: No module named 'tqdm'The gamba transaction analysis library is a collection of methods for applying analytical methods found in peer-reviewed studies. The library is 100% free and open source, and aims to be used by researchers and analysts with access to transaction data sets.Featuresstandardise your data across game types and activitiescompute behavioural measuresapply descriptive and comparative statisticsrun machine learning methodsreplicate full academic studies on your data in minutesGetting StartedGetting started using gamba in your research is super easy - the first step is to install the library using one of the commands below. From there, read through one or more of the tutorials, and once you're familiar with the typical structure of an analysis using the gamba library, look through the individual module documentation and extend one of the examples. If you get stuck, pleaseget in touch!InstallTo install gamba use the following pip command;pip install gambaOr if you'd like to use the development release, clone the github repository to your machine;git clone https://github.com/gamba-dev/gamba.git
Package: gambas
GambasGambas is a config key validator. You can confirm the config file whether its keys all exist or not.Generally,default.cfgwould be the standard for your config file. (Of course, you don't need to setdefault.cfgas a standard file name.) And, you has a config file used on your project, but not upload on your git due to.gitignore. I'll call it 'target config file'. Because it is a little bothering to sync two files, you sometimes might miss some keys.Comparing two files' keys withGambas, you can find the keys you missed of the target config file.Getting StartedBuilt WithPython >= 3.6.xInstallationInstall Gambas with pip command>pipinstallgambasUsagePrepare your 2 config files, default file and target file. The file extension is limited only oncfg,json.# cfg file
PSQL_HOST="127.0.0.1"
PSQL_PORT="5432"
PSQL_DBNAME="test_db"
WEBHOOK_URL="http://webhook.url.com"
...# json file
{
    "psql_host": "127.0.0.1",
    "psql_port": "5432",
    "psql_dbname": "test_db",
    "webhook_url": "http://webhook.url.com",
}
...Execute the command with config files path.-d: the default config file-t: the target config file>gambas-ddefault_config.cfg-ttarget_config.cfgYou also can compare files with different extensions.>gambas-ddefault_config.cfg-ttarget_json.jsonIf you don't want to let an error raise, execute the command with option--no-error. In this case, the warnig comes out instead of an error message.>gambas-ddefault_config.cfg-ttarget_config.cfg--no-errorContactChung Yunyoung -yy.chung@linewalks.com