Saravanan666/pypi-org huggingface dataset:
Package: gblearn
API DocumentationRecently, we proposed a universal descriptor for grain boundaries that
has desirable mathematical properties, and which can be applied to
arbitrary grain boundaries. Using this descriptor, we were able to
create a feature matrix for machine learning based on the local atomic
environments present at the grain boundary. In addition to being useful
for predicting grain boundary energy and mobility, the method also
allows important atomic environments to be discovered for each of the
properties.If you use this package, please cite the paper:@article{Rosenbrock:2017vd,
author = {Rosenbrock, Conrad W and Homer, Eric R and Csanyi, G{\'a}bor and Hart, Gus L W},
title = {{Discovering the building blocks of atomic systems using machine learning: application to grain boundaries}},
journal = {npj Computational Materials},
year = {2017},
volume = {3},
number = {1},
pages = {29}
}You can generate the Local Environment Representation for the Olmsted
dataset using the following code. It assumes that all the Olmsted [1]
dump files from LAMMPS are in/dbs/olmsted. We tell the framework to
store all representations in the/gbs/olmstedfolder.# Load the perfect FCC as a seed so the LER can be constructed.# It assumes the the seed file is found at /seeds/"Ni.pissnnl_seed.txt"seed=np.loadtxt("/seeds/Ni.pissnnl_seed.txt")fromgblearn.gbimportGrainBoundaryCollectionasGBColmsted=GBC("olmsted","/dbs/olmsted","/gbs/olmsted",r"ni.p(?P<gbid>\d+).out",seed=seed,padding=6.50)# We explicitly call :meth:`load` to parse the GB files. Then, construct# the SOAP representation for each GB.# As part of the load function, we call it with Z=28 for the nickel database,# and also give it a method and pattern to useolmsted.load(Z=28,method="cna",pattr="c_cna")# Calculate the SOAP representation.# The SOAP representation includes padding around the boundary atoms, so# that each atom in the GB has a full `rcut` of atoms around it.# The "meth: 'soap' auto trims those atoms that don't have full environments.olmsted.soap(rcut=3.25,lmax=12,nmax=12,sigma=0.5)#Now, we can finally construct the LER.olmsted.LER(0.0025)References[1]: Olmsted, D. L., Foiles, S. M. & Holm, E. A. Survey of computed
grain boundary properties in face-centered cubic metals: I. Grain
boundary energy. Acta Mater. 57, 3694–3703 (2009).
Package: gblinks
Broken links!Oh man, Gitbook is such a nice tool to handle your documentation, but it’s so easy to mess it up and add broken links here and there, right?Gblinks is a easy to use tool to detect those links on time so you can fix them before publishing your documentation. The process can even be integrated in your CI workflow!Cool, where do I get it?Make it easy and get it from pip:$ pip install gblinksIf the version there is a bit outdated or you need a patch that is not in master yet, you can get it directly from GitHub:$ git clone https://github.com/davidmogar/gblinks.git
$ cd gblinks
$ python setup.py installAnd now what?Now you use it ;)This is the easier part. You only have to run the next command:$ gblinks -v PATH_TO_YOUR_GITBOOKThe output will be something similar to this:[{"file":"my_gitbook/README.md","link_path":"my_gitbook/chapter2/README.md","link_text":"chapter 2","link_url":"#chapter2"},{"file":"my_gitbook/README.md","link_path":"my_gitbook/chapter10/contributors.md","link_text":"Contributors for this chapter","link_url":"#contributors"}]You can also remove the verbose flag to get only a warning with the number of broken links.If your want to check what options you can use with gblinks, just run the next command:$ gblinks --helpI have a fix/new featureThat’s great. Contributors are always welcomed. Just send a pull request and be part of gblinks!
Package: gblpyapi
gblapi.py - The official Python API Wrapper for the GBL APIAboutThis is theofficialAPI wrapper for GlennBotList.xyz written in Python and published toPyPi.InstallationTo installgblapi.py, run this command in a terminal:$ pip install gblpyapiFeaturesglennbotlist.WebhookServergblpyapi now has a webhook server! Create an instance of the WebhookServer module and pass the required parameters. Get when a user votes with the event below!Event NameParameterson_glenn_votedataglennbotlist.GBLMethodActionpost_guild_count()(note: requires authentication)POST guild countfetch_user_info(user_id)GET user infofetch_has_voted(user_id)(note: required authentication)GET if a user has votedfetch_bot_votes()(note: requires authentication)GET bot votesfetch_bot_stats(bot_id)GET bot statsfetch_vote_count()(note: requires authentication)GET bot vote countExamplesTo easily combine all examples, all examples will be shown in a discord.ext.commands.CogfromglennbotlistimportGBLfromglennbotlistimportWebhookServerimportdiscordfromdiscord.extimportcommands,tasksclassGlennBotList(commands.Cog):def__init__(self,bot):self.bot=botself.glenn=GBL(bot=self.bot,token=config.gbl,logging=True)# Replace "GBL TOKEN" with your GBL Token. True enables logging.self.server=WebhookServer(self.bot)# Initiate Webhook Serverself.postservers.start()# Starts the postservers task@commands.Cog.listener()asyncdefon_glenn_vote(self,data):# Triggered whenever a user votesprint(data)@tasks.loop(hours=1)asyncdefpostservers(self):awaitself.glenn.post_guild_count()# Post you server count and shard count. If logging is enabled, it will print a success message@commands.command()asyncdefuser_info(self,ctx,user:discord.Member):ifuser.bot:returnawaitctx.send("This user is not a bot")guser=awaitself.glenn.fetch_user_info(user.id)# Returns a glennbotlist.User objectawaitctx.send(f"Name:{guser.name}. Bio:{guser.bio}")# Sends the users name and bio. For all User attributes, print the directory of glennbotlist.User@commands.command()asyncdefhas_voted(self,ctx,user:discord.Member):ifuser.bot:returnawaitctx.send("This user is not a bot")voted=awaitself.glenn.fetch_has_voted(user.id)# Returns a bool of whether the user has votedawaitctx.send(f"{user}'s vote status:{voted}")@commands.command()asyncdefvotes(self,ctx):votes=awaitself.glenn.fetch_bot_votes()# Gets a dict of votesawaitctx.send(votes)@commands.command()asyncdefbotinfo(self,ctx,user:discord.Member):ifnotuser.bot:awaitctx.send("This is not a bot!")bot=awaitself.glenn.fetch_bot_stats(user.id)# Returns a glennbotlist.Bot objectawaitctx.send(f"Name:{bot.name}. Prefix:{bot.prefix}")# Sends the bots name and prefix. For all Bot attributes, print the directory of glennbotlist.Bot@commands.command()asyncdefvote_count(self,ctx):votes=awaitself.glenn.fetch_vote_count()# Gets all time and monthly votesawaitctx.send(f"I have{votes['monthly']}votes this month!")# Sends amount of votes for the current monthdefsetup(bot):bot.add_cog(GlennBotList(bot))AuthorsMade with ❤️ by A Trash Coder#0214 and A Discord User#1173.HelpJoin theDiscordfor more help on this module.
Package: gbm-autosplit
gbm_autosplitGBM scikit-learn interfaces which performs "early stopping" with single data set duringfit."Early stopping" is great practice to tune the number of estimators for gradient boosting models.
However it is not difficult to use it in tuning module in scikit-learn such as RandomizedSearchCV / GridSearchCV
because to use early stopping module requires two data sets but scikit learn does not have such interface.To solve this situation, this interface performs following steps with infit.Split original input data into two randomlyEstimaten_estimatorsby using split data set with early stoppingPerformfitby using entire data set with estimatedn_estimatorsInstallpip install gbm_autosplitUsageimport gbm_autosplit

estimator = gbm_autosplit.LGBMClassifier()
estimator.fit(x, y)
Package: gbm-drm-gen
GBM Response GeneratorA fast, python based response generator for the Fermi-GBM. A 3ML plugin is also provided that allows for one to locate GRBs with any of the GBM spectral data.If you find this code useful or use it please cite the following works:Burgess et al 2018https://arxiv.org/abs/1902.01082Installpipinstallgbm_drm_gen
Package: gb-menu
Welcome to gb-menu 👋A lightweight Python library useful for creating powerful yet simple console-based user interfacesInstallpipinstallgb-menuUsagefromgb_menuimportmenu,choice,actionmain_menu=menu.Menu()cont_action=action.Action(function=type(None))cont_choice=choice.Choice(key='c',text='Continue',action=cont_action)main_menu.add_choice(cont_choice)quit_action=action.Action(function=exit)quit_choice=choice.Choice(key='q',text='Quit',action=quit_action)main_menu.add_choice(quit_choice)whileTrue:main_menu.show()Outputc:Continue
q:Quit
Choice:c
c:Continue
q:Quit
Choice:c
c:Continue
q:Quit
Choice:q

Processfinishedwithexitcode0Author👤Garrett BeardGithub:@gbeard2LinkedIn:@garrett-beard🤝 ContributingContributions, issues and feature requests are welcome!Feel free to checkissues page.Show your supportGive a ⭐️ if this project helped you!📝 LicenseCopyright © 2021Garrett Beard.This project isMITlicensed.This README was generated with ❤️ byreadme-md-generator
Package: gbmessclient12345
Клиентское приложение мессенджера. Подключается к серверу и обменивается с ними сообщениями. Имеет графический интерфейс.Запуск клиентаЗапуск клиента выполняется из корневой директории путем вызова модуляclient.pyиз командной строки с параметрами:-a / –host - Адрес сервера.-b / –port - Порт сервера.-u / –user - Логин пользователя.-p / –password - Пароль пользователя.В данном режиме не поддерживается ввод команд.Примеры использования:python client.py-a127.0.0.1-b8080Запуск клиента для подключения к серверу по адресу 127.0.0.1:8080python client.py-a127.0.0.1-b8080-ulogin1-ppassword1Запуск клиента для подключения к серверу по адресу 127.0.0.1:8080 с учетными данными login1/password1client.pyЗапускаемый модуль,содержит парсер аргументов командной строки и функционал инициализации приложения.
Package: gbmessserver12345
Серверное приложение мессенджера. Обрабатывает подключения клиентов и обменивается с ними сообщениями. Имеет графический и консольный интерфейсы администратора.Запуск сервераЗапуск сервера выполняется из корневой директории путем вызова модуляserver.pyиз командной строки с параметрами:-p / –port - Порт, на котором принимаются соединения-a / –host - Адрес, с которого принимаются соединения.-s / –silent Запуск без графической оболочки.В данном режиме не поддерживается ввод команд.Примеры использования:python server.py-p8080Запуск сервера на порту 8080python server.py-alocalhostЗапуск сервера принимающего только соединения с localhostpython server.py-sЗапуск без графической оболочкиАдминистрированиеВедение пользователей выполняется в графической оболочке, создание может выполняться в интерфейсе командной строки.
Для создания пользователя из интерфейса командной строки необходимо вызвать модульserver.py, указав командуuser_addс параметрами:
1. -l / –login - Логин пользователя;
2. -p / –password - Пароль.python server.py user_add-ltest2-p123Создание пользователя test2 с паролем 123server.pyЗапускаемый модуль,содержит парсер аргументов командной строки и функционал инициализации приложения.
Package: gbmgeometry
gbmgeometryRoutines to handle GBM geometry and plotting.provides the ability to plot Fermi's orbitenables the reading and interpretation of GBM spacecraft filescomputes GRB emission blockage by components of the spacecraftSpecial thanks toBjoern BiltzingerandFrancesco Berlatofor helping me with crazy geometry in multiple coordinate systems.Install$>pipinstallgbmgeometry
Package: gbm-org-analysis
gbm_org_analysisDesigned to analyze glioblastoma (GBM) cell and brain organoid data exported from Imaris.More specifically this package extracts, performs relavent comparative statistics, and generates preliminary figures for the following data: 1) total number of GBM cells; 2) distance of each cell to the nearest surface of the organoid (distance to surface); 3) number of GBM cells whose distace to surface exceeds a defined invasion threshold; 4) brain organoid volume; 5) brain organoid surface area.Installation$pipinstallgbm_org_analysisReproducing experimental analysis with test datasetSet up new virtual environement (if desired)Download and unzip test dataset$curl-Lhttps://miami.box.com/shared/static/uzm30l05xwjhx2v9qeie9ruc12siz00f.zip--outputGbm_TestData.zip
$unzipGbm_TestData.zipNavigate to data directory and install requirements$cdGbm_TestData
$pipinstall-rrequirements.txtRun analysis$python3test_analysis.pyNotes:Step 3: requirements.txt includes instructions for installing gbm_org_analysis packageStep 4: test_analysis.py automatically exports processed data, statistical results and preliminary figures to three new sub directoriesFull datasets will be made available for download as they are publishedCompiled code:curl-Lhttps://miami.box.com/shared/static/uzm30l05xwjhx2v9qeie9ruc12siz00f.zip--outputGbm_TestData.zip
unzipGbm_TestData.zipcdGbm_TestData
pipinstall-rrequirements.txt
python3test_analysis.py
Package: gbmplus
GBM Plus API Python LibraryThe GBM Plus API Python library aims to provide all current API calls to interface with the GBM Plus/Homebroker platform.SetupGet the CLIENT_ID from the GBM Plus login dashboardKeep your USER_EMAIL, USER_PASSWORD and CLIENT_ID safe and secure. You can use environment variables for development.Install the latest version ofPython 3Usepip(or an alternative such aseasy_install) to install the library from the PythonPackage Index:pip install gbmplusIf you have both Python3 and Python2 installed, you may need to usepip3(sopip3 install gbmplus) along withpython3on your systemIf gbmplus was previously installed, you can upgrade to the latest non-beta release withpip install --upgrade gbmplusUsageExport your USER_EMAIL, USER_PASSWORD and CLIENT_ID asenvironment variables, for example:LinuxexportUSER_EMAIL="xxxxxx@xxxxx.com"exportUSER_PASSWORD="shabadabadashabadabadaenelcentrodelplaneta"exportCLIENT_ID="xxxxxxxxxxxxxxxxxxx"Windows Powershell$env:USER_EMAIL="xxxxxx@xxxxx.com"$env:USER_PASSWORD="shabadabadashabadabadaolvidadoenlabanqueta"$env:CLIENT_ID="xxxxxxxxxxxxxxxxxxx"Alternatively, define your USER_EMAIL, USER_PASSWORD and CLIENT_ID as variables in your source code; this method is not recommended due to its inherent insecurity.Single line of code to import and use the library goes at the top of your script:importgbmplusInstantiate the client (API consumer class), optionally specifying any of the parameters available to set:trader_object=gbmplus.GBMPlusAPI(output_log=False)Make API calls in your source code, using the formatclient.scope.operation, whereclientis the name you defined in the previous step (trader_objectabove),scopeis the corresponding scope that represents a module, andoperationis the operation of the API endpoint. For example, to make a call to get the list of Accounts (or Strategies), use this function call:accounts=trader_object.accounts.getAccounts()print(accounts)ExamplesYou can find working example scripts in theexamplesfolder.ScriptPurposetransfer.pyTransfer from the Smart Cash strategy to a target strategysubmitOrder.pySubmit a buy order, issuer: FUNO 11, trading type: market, instrument_type: IPCtradingUSAExample.pySubmit a buy order with Trading USA, ticker: AMZN
Package: gbn
Greenlet BottleNeck profiler.Measures time precisely using “greenlet.settrace” to pause/continue counting time on switch from/to original greenlet.May count wall-clock time too. Also counts step calls and context switches.Usage:gbn_attach()

gbn('step1')
step1()

gbn('step2')
step2()

wall = gbn('step3')
step3()

wall = gbn('step4', wall=wall)
step4()

gbn('step5', wall=wall)
step5()

gbn()
does_not_count_this()

log.info(gbn_report_and_reset())
# OR
spawn(gbn_report_and_reset, each=60, log=log.info)Seehelp(gbn)for detailed docs.Additional tools:gxray- Heavy profiler when you don’t know where to profile.gswitched- Checks if greenlet switched.
Package: gbnf-compiler
GBNF Compiler (Python)Dependency-free GBNF Compiler, plug and play for llama.cpp GBNF.Simple yet powerful GBNF Compiler, use it like handlebars.js with better response from LLMs.Why ?GBNF is very useful to confine the response format from LLMs.In most of the time, using sentence-based GBNF can produce better result than JSON-based GBNF in most LLMs without fine-tuning,gbnf-compilerprovides the flexibility to construct or parse sentence / JSON / (anything you can think of) GBNF.Getting Startedpip install gbnf-compilerHow to UseDefine the LLM Response TemplateCreate the RuleSend it out, done!importrequestsfromgbnf_compilerimport*# Define your Promptprompt="What tool will you use to calculate 2^5 ?"# Define the LLM Response Template# Each {{}} is a variable with a ruletemplate="I choose {{tool}} because {{reason}}"tools=MultipleChoice('tool',['calculator','web-search','web-browse'])c=GBNFCompiler(template,{'tool':tools,'reason':SingleSentence()})print(c.grammar())# Try a dummy resulttext="I choose calculator because it is the most efficient and accurate way to calculate 2^5."result=c.parse(text)print(result)"""Result:{'tool': 'calculator', 'reason': 'it is the most efficient and accurate way to calculate 2^5.'}"""# Example: Send it out to local llama.cppdeftemplate(role:str,prompt:str):return"""
 
{role}
{prompt}
""".format(role=role,prompt=prompt)data_json={"prompt":template("",prompt),"temperature":0.0,"n_predict":512,"top_p":0.2,"top_k":10,"stream":False,"grammar":c.grammar()}resp=requests.post(url="http://127.0.0.1:9999/completion",headers={"Content-Type":"application/json"},json=data_json,)result=resp.json()["content"]print(c.parse(result))Useful Rule ExamplesItemList: The Result will automatically compiled into a list of string.Drawback: The LLM might keep generating more items until max tokens.Point Form: Create a Point Form Result with specific numbers.This can restrict the LLM to provide limited items.
Package: gbnnetwork
BNNetwork LibraryEsta librería permite crear redes Bayesianas a partir de la probabilidad condicional de cada nodo.Instalarpip install gbnnetworkUsoPara crear una Red de Inferencia, se deben de seguir los siguientes pasos:Crear una instancia de la clase BNNetworknetwork = BNetwork()Agregar variables a la rednetwork.add_variable("A")Asignar los padres de cada variable (en caso la variable no tenga padres este paso se puede omitir)network.set_parents_to_variable("A", ["B", "E"])Agregar las probabilidades a las variables para definir la red totalmente.Toda la red debe de estar definida si se desea responder cualquier consultanetwork.add_probability("A|BE", 0.001)Hacer una queryp = network.inference({'B': False}, {'A': False})(Si se desea hacer una query sin valores observados, el diccionario se debe enviar vacío)Funciones De UtilidadObtener la representación compactanetwork.compact_string()(string)Obtener los factores de la rednetwork.factor_string()(string)Saber si la red esta totalmente definidanetwork.validate_defined_state()(boolean)EjemploTomando como referencia el siguiente documento:https://people.cs.pitt.edu/~milos/courses/cs2740/Lectures/class19.pdfLa red que se propone tiene la siguiente topología:Se procede de la siguiente manera:fromgbnnetworkimportBNetworknetwork=BNetwork()# Paso 1# Paso 2network.add_variable("B")network.add_variable("E")network.add_variable("A")network.add_variable("J")network.add_variable("M")# Paso 3network.set_parents_to_variable("A",["B","E"])network.set_parents_to_variable("J",["A"])network.set_parents_to_variable("M",["A"])# Paso 4network.add_probability("B",0.001)network.add_probability("E",0.002)network.add_probability("A|BE",0.95)network.add_probability("A|-BE",0.29)network.add_probability("A|B-E",0.94)network.add_probability("A|-B-E",0.001)network.add_probability("M|A",0.7)network.add_probability("M|-A",0.01)network.add_probability("J|A",0.9)network.add_probability("J|-A",0.05)# Hacer la query P(B=True|J=True,M=True)p=network.inference({'B':True},{'J':True,'M':True})# 0.2841718353643929 = 28%
Package: gboml
GBOML: Graph-Based Optimization Modeling LanguageContentsAboutQuick startDocumentationCommunity guidelinesCiting GBOMLLicenseAboutThe Graph-Based Optimization Modeling Language (GBOML) is a modeling language for mathematical programming enabling the easy implementation of a broad class of structured mixed-integer linear programs typically found in applications ranging from energy system planning to supply chain management. More precisely, the language is particularly well-suited for representing problems involving the optimization of discrete-time dynamical systems over a finite time horizon and possessing a block structure that can be encoded by a hierarchical hypergraph. The language combines elements of both algebraic and object-oriented modeling languages in order to facilitate problem encoding and model re-use, speed up model generation, expose problem structure to specialised solvers and simplify post-processing. The GBOML parser, which is implemented in Python, turns GBOML input files into hierarchical graph data structures representing optimization models. The associated tool provides both a command-line interface and a Python API to construct models, and directly interfaces with a variety of open source and commercial solvers, including structure-exploiting ones.Quick startGBOML has been tested on Windows, macOS and Linux. The simplest way to install it is via thepippackage manager in a terminal window:pip install gbomlPlease refer to the online documentation for moreinformation on how to install GBOML. Note that optimization solvers are also required to solve GBOML models and installation instructions are also provided in the online documentation.Several examples are provided in this repository (in the examples directory) and explained in theonline documentation.DocumentationDocumentation is available on Read the Docs:Read the documentation onlineCommunity guidelinesTo contribute changes:Fork the project on GitlabCreate a feature branch to work on in your fork (git checkout -b new-feature)Commit your changes to the feature branchPush the branch to Gitlab (git push origin my-new-feature)On Gitlab, create a new pull request from the feature branchIssues and questions shall be reported in theissues directory.Citing GBOML@article{Miftari2022,
  doi = {10.21105/joss.04158},
  url = {https://doi.org/10.21105/joss.04158},
  year = {2022},
  publisher = {The Open Journal},
  volume = {7},
  number = {72},
  pages = {4158},
  author = {Bardhyl Miftari and Mathias Berger and Hatim Djelassi and Damien Ernst},
  title = {GBOML: Graph-Based Optimization Modeling Language},
  journal = {Journal of Open Source Software}
}LicenseCopyright (C) since 2020 Bardhyl Miftari, Mathias Berger, Hatim Djelassi, Damien Ernst, University of Liege .   Licensed under the MIT License (see LICENSE file).
Package: gbp
Tools to ease the development of Debian and (partially) RPM packages in git
repositories.  For documentation see the manual online at:https://gbp.sigxcpu.org/manual/On Debian systems, the documentation can be found in
/usr/share/doc/git-buildpackage/manual-html.The API documentation of the gbp module can be found at:https://gbp.sigxcpu.org/apidocs/The mailing list is at:https://lists.sigxcpu.org/mailman/listinfo/git-buildpackagegit-buildpackage at lists.sigxcpu.orgSee the HACKING document for details on contributing to gbp development. The
package is also available on Pypi at:https://pypi.python.org/pypi/gbp/![gbp logo](docs/gbp.svg)[![Build Status](https://travis-ci.org/agx/git-buildpackage.svg?branch=master)](https://travis-ci.org/agx/git-buildpackage)
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