Saravanan666/pypi-org huggingface dataset:
Package: farialimer
farialimerParser / Writer for Brazilian Finance DocumentsIntroBem-vindo ao projeto de CNAB e IMBARQ! Este projeto tem como objetivo fornecer uma solução open source para processar e gerenciar arquivos de remessa e retorno de cobranças bancárias, de acordo com as normas CNAB e IMBARQ.Chegou a hora de unificar nossos esforços e resolver de uma vez por todas essa implementação de layouts.Nossa equipe acredita que a transparência e a colaboração são essenciais para o desenvolvimento de soluções financeiras eficientes e confiáveis. Nós convidamos a comunidade a participar deste projeto, compartilhar ideias, reportar bugs e contribuir com código. Juntos, podemos criar uma solução robusta e de alta qualidade para o setor financeiro.CNABCNAB significa "Centro Nacional de Automação Bancária". É um padrão de arquivo utilizado por bancos no Brasil para a transferência de informações financeiras, como cobranças, pagamentos, boletos bancários, entre outras. O CNAB é um padrão estabelecido pela Febraban (Federação Brasileira de Bancos) que padroniza a forma como os arquivos são estruturados, tornando mais fácil e ágil a integração entre sistemas de diferentes bancos. O uso do CNAB permite a automação de processos bancários, melhorando a eficiência e a segurança nas transações financeiras.IMBARQIMBARQ significa "Interface para Movimentação Bancária de Retorno e Quitação". É um padrão de arquivo utilizado pela B3 (Bolsa Brasileira de Valores) para a transferência de informações financeiras entre instituições financeiras. O IMBARQ é utilizado para facilitar a integração de sistemas de diferentes bancos, permitindo a realização de operações bancárias de forma mais ágil e segura.Outros ProjetosUma lista com outros projetos que serviram de inspiração:https://github.com/Trust-Code/python-cnabhttps://github.com/kivanio/brcobrancahttps://github.com/kmee/cnab240https://github.com/vintasoftware/auroraehttps://github.com/glauberportella/cnab-layouts
Package: faris-lab-train-model
nnGA Library - Neural Network Genetic Algorithm Library (v0.0.5)Off the shelf Genetic Algorithm library for deep learning problemsLicenseOur code is released under the MIT license (refer to theLICENSEfile for details).RequirementsTo use the library you need atleast Python 3.6. Examples may require additional libraries.Other required dependencies:NumPyNeptuneUsage/ExamplesYou can import the library by typingpip install faris-lab-train-model.To learn how to use neptune, check the following examples:importneptune.newasneptunefromnngaimportnnGA,GaussianInitializationStrategy,\GaussianMutationStrategy,BasicCrossoverStrategy,\PopulationParametersdefmake_network(parameters=None):''' Function that creates a network given a set of parameters '''neural_network=...returnneural_networkdeffitness(idx,parameters):''' Fitness function to evaluate a set of parameters '''# Evaluate parametersnetwork=make_network(parameters)returnevaluate_network(network)if__name__=='__main__':# Initialize GA parametersnetwork=make_initial_network()network_structure=[list(layer.shape)forlayerinnetwork]# List of tuples, containing the shape of each layer# Population parameterspopulation=PopulationParameters(population_size=200)# Mutation strategymutation=GaussianMutationStrategy(network_structure,1e-1)# Crossover strategycrossover=BasicCrossoverStrategy(network_structure)# Initialization strategyinit=GaussianInitializationStrategy(mean=0.,std=1.,network_structure=network_structure)ga=nnGA(epochs=50,# Number of epochsfitness_function=fitness,population_parameters=population,mutation_strategy=mutation,initialization_strategy=init,crossover_strategy=crossover,num_processors=8)# Number of cores# Run GA with neptunerun=neptune.init(project="common/quickstarts",api_token="ANONYMOUS",ga)In general the code has the following structurefromnngaimportnnGA,GaussianInitializationStrategy,\GaussianMutationStrategy,BasicCrossoverStrategy,\PopulationParametersdefmake_network(parameters=None):''' Function that creates a network given a set of parameters '''neural_network=...returnneural_networkdeffitness(idx,parameters):''' Fitness function to evaluate a set of parameters '''# Evaluate parametersnetwork=make_network(parameters)returnevaluate_network(network)if__name__=='__main__':# Initialize GA parametersnetwork=make_initial_network()network_structure=[list(layer.shape)forlayerinnetwork]# List of tuples, containing the shape of each layer# Population parameterspopulation=PopulationParameters(population_size=200)# Mutation strategymutation=GaussianMutationStrategy(network_structure,1e-1)# Crossover strategycrossover=BasicCrossoverStrategy(network_structure)# Initialization strategyinit=GaussianInitializationStrategy(mean=0.,std=1.,network_structure=network_structure)ga=nnGA(epochs=50,# Number of epochsfitness_function=fitness,population_parameters=population,mutation_strategy=mutation,initialization_strategy=init,crossover_strategy=crossover,num_processors=8)# Number of cores# Run GAnetwork_parameters,best_result,results=ga.run()
Package: farm-compiler
The Farm Compiler is written in python and has three main functions:def compile_string(string): ~Takes a python string of Farm commands and compiles it.
def compile_file(name): ~Compiles a text file, or any file with farm_compiler commands in it.
def write_to_file(string, name): ~Takes a python string, compiles it, and writes the
resulting farm_compiler result to the name specified.
Package: farm-contact
OverviewA django application which provides a contact form which sends an email and stores its details in the database.InstallationInstall withpip installfarm-contact.Addcontactto your installed apps.Addcontact.context_processors.contact_formto yourTEMPLATE_CONTEXT_PROCESSORS.SetENQUIRY_SUBJECT="Enquiry"or whatever you want it to be (defaults to Contact Form Enquiry).Runmanage.py syncdb.Runmanage.py migrate contact.UsageFirst you will need to add at least oneEnquiry typein the admin.
You can then either include the default template like this:{% include 'contact/index.html' %}Or create your own.Contactjon@wearefarm.com
Package: farmer
FarmerUsefarmerto deploy your applications onVM Farms.InstallationInstall with pip:pip install farmerConfigurationYou need to provide Farmer with your VM Farms API token. You can retrieve your API token from theAPI documentation sectionof the VM Farms portal.Runfarmer configto set your token for the first time:farmer configIf you need to change your token for any reason, you can usefarmer config set token:farmer config set token c422b5e2230d617d22759a19a5a5cb65792edebcYou can also set the token using theFARMER_TOKENenvironment variable:FARMER_TOKEN=c422b5e2230d617d22759a19a5a5cb65792edebc farmer appsUsagefarmer appsRunfarmer appsto list your  applications.farmer appsIf you don't see any applications, we probably need to connect a few wires for you. Contact oursupport teamand we'll sort you out.farmer deployRunfarmer deployto deploy an application:farmer deploy api api-prodfarmer logdnaExport logs fromLogDNAinJSONLines format.First, generate aservice keyand configure Farmer:farmer logdna configTo export logs, runfarmer logdna export.
You can filter results by application, host, log level, or a custom search query.# Export all PostgreSQL logs from the past week.
farmer logdna export --from 'last week' --app postgres

# Export deploy logs from today.
farmer logdna export --app deployThe-f/--fromand-t/--tooptions support human readable dates like "1 hour ago", "30 minutes ago", "yesterday", etc.
Refer to thedateparser documentationfor more information.Getting helpTo get help for a specific command or subcommand, runfarmer help:farmer help deployfarmer help logdna configFor bugs or feature requests related to Farmer itself, please open aGitHub issue.For issues related to your applications or deploys, please contactVM Farms support.TricksAdd this snippet to your Bash configuration (~/.bashrcor~/.bash_profile) to enable tab-completion:eval"$(_FARMER_COMPLETE=sourcefarmer)"Enjoy!LicenseApache 2.0
Package: farm-faq
OverviewA django application which provides all you need to implement an FAQs page. Includes django-cms hooks.InstallationInstall withpip installfarm-faq.Addfaqto your installed apps.Addfaq.context_processors.faq_categoriesto yourTEMPLATE_CONTEXT_PROCESSORS.Runmanage.py syncdb.Runmanage.py migrate faq.UsageFirst you will need to add at least oneFAQ Questionin the admin.
You can then either include the default templates like this:List of questions{% include 'faq/list.html' %}Questions and answers{% include 'faq/items.html' %}Or create your own.LicenseImperavi Redactor is licensed under Creative Commons Attribution-NonCommercial 3.0 license.For commercial use please buy license here:http://redactorjs.com/download/or use earlier version.## Contactjon@wearefarm.com
Package: farm-haystack-speech2text
speech2textTable of ContentsInstallationUsageLicenseInstallationpip install farm-haystack-speech2textInstallationStep 1. Install the audio system dependencies. On Debian-based systems:sudo apt-get install libsndfile1 ffmpegStep 2. Install the package. With pip:pip install farm-haystack-speech2textLicensehaystack-speech2textis distributed under the terms of theApache-2.0license.
Package: farm-haystack-text2speech
text2speechTable of ContentsInstallationUsageLicenseInstallationpip install farm-haystack-text2speechInstallationStep 1. Install the audio system dependencies. On Debian-based systems:sudo apt-get install libsndfile1 ffmpegStep 2. Install the package. With pip:pip install farm-haystack-text2speechLicensehaystack-text2speechis distributed under the terms of theApache-2.0license.
Package: farmlayers
Farm Layers [early stages of dev]Helper scripts for fetching and managing basic geotiff files related to Farm MappingSupports:elevationred bandsblue bandsgreen bandsnear-inferred bandsComing:Australian Radiometric datak gammau gammath gammaInstall:pip install farmlayersUsage:Make area of interest using shapely geoms:fromshapely.geometry.multipolygonimportMultiPolygonfromfarmlayersimportshp_to_boundariesboundaries=shp_to_boundaries("boundaries.shp")Download elevation:fromfarmlayersimportdownload_elevationdownload_elevation(geom=MultiPolygon(polygons=boundaries),dir="data")Download soil related landsat 8 bands:fromfarmlayersimportdownload_soildownload_soil(geom=MultiPolygon(polygons=boundaries),dir="data")Download crop related landsat 8 bandsfromfarmlayersimportdownload_cropdownload_crop(geom=MultiPolygon(polygons=boundaries),dir="data")
Package: farm-news
OverviewA django application which provides news articles and related views.InstallationInstall withpip installfarm-news.Addnewsto your installed apps.SetNUM_LATEST_ARTICLESin your settings, default is 3.Addnews.context_processors.latest_articlesto yourTEMPLATE_CONTEXT_PROCESSORS.Runmanage.py syncdb.Runmanage.py migrate news.UsageFirst you will need to add at least oneArticlein the admin.Then override the templates by creating new ones in your own app’s template directory
* Article list found at /news/news/article_list.html* Article detail found at /news/<slug>news/article_detail.htmlYou can also include the following template like this:
* Article list (useful for including in a sidebar){% include 'news/article_list.html' %}Or create your own.LicenseImperavi Redactor is licensed under Creative Commons Attribution-NonCommercial 3.0 license.For commercial use please buy license here:http://redactorjs.com/download/or use earlier version.Contactjon@wearefarm.com
Package: farm-ng-amiga
farm-ng-amigaInstallFrom pippipinstallfarm-ng-amigaFrom sourceWe recommend running the brain SDK applications in a virtual environment to avoid conflicts with other packages / versions installed on your system.
Though this is not a requirement and you are welcome to decide how/where to install.Installpip3&virtualenv:sudoapt-getinstallpython3-pip
sudopip3installvirtualenvClone the project:gitclonehttps://github.com/farm-ng/farm-ng-amiga.gitStart a virtual environment:# assuming you're already in the farm-ng-amiga/ directorypython3-mvenvvenvsourcevenv/bin/activateCreate and install thefarm_ng\s (brain) Python package# install to systempip3install.# or for development modepip3install-e.[dev]
Package: farmOS
farmOS.pyfarmOS.py is a Python library for interacting with farmOS servers over API.For more information on farmOS, visitfarmOS.org.InstallationTo install usingpip:$pipinstallfarmOS>=1.0To install usingcondaseeconda-forge/farmos-feedstockDocumentationfarmOS.py can connect to farmOS servers running version ^1.6 or 2.x. The version should be specified when instantiating
the farmOS client.1.x documentation2.x documentationMAINTAINERSPaul Weidner (paul121) -https://github.com/paul121Michael Stenta (m.stenta) -https://github.com/mstentaThis project has been sponsored by:FarmierPennsylvania Association for Sustainable AgricultureOur SciBionutrient Food AssociationFoundation for Food and Agriculture Research
Package: farmtool
FarmtoolFarmtool to help the farmer to automate and handle Cowsheds.InstallationUse the package manager to install poetry() to install all the needed dependencies for the project.pipinstallpoetryUse poetry package to install all dependencies for the project.poetryinstallExecute code and testsThis step should be only executed after the installation is finished.connect to the project environment.poetryshelland then you can run the code or test it using the following commands.Run codeuvicornfarmtool.main:app--reloadAfter running the code, a session will be opened in the followingURL- Recomendation:Usedocsto load the swaggerUI to test endpoints seamlessly.Test codepytestTry it out:A publicly deployed and running version of the farmtool is available on azure.check it out:farmtool
Package: farmware-tools
Farmware ToolsTools for use byFarmware.Starting with FarmBot OS v6.4.2, this package is installed automatically and available for Farmware to import.See the sections below for other installation configurations.Examplefromfarmware_toolsimportdevicedevice.log('hello')If the above code is run within a Farmware,FarmBot OSwill send ahellolog to theFarmBot Web App.If the above code is run elsewhere,
the output will be the command that would have been sent to FarmBot OS:{'kind': 'send_message', 'args': {'message': 'hello', 'message_type': 'info'}}DocumentationIn addition to theFarmware development documentation,
a list of available functions is available via the Python console:fromfarmware_toolsimportapp,devicehelp(app)help(device)Local installation (for local Farmware development)pip install farmware-toolsuser-level install:pip install --user farmware-toolsupgrade:pip install --user --upgrade farmware-toolsinstall a specific version:pip install --user farmware-tools==1.0.0Specifying a version in a Farmware manifest"farmware_tools_version": "v1.0.0"By including this line in a Farmwaremanifest,
FarmBot OS will automatically install the specified version
and make it available to import from within the Farmware.
Package: farmworld
FarmWorldA reinforcement learning library for agriculture.HOWTOpipinstallfarmworldInstall from sourcemake venv
make installBuild/PublishPut a new release on Githubpoetrybuild
poetrypublishTestPYTHONPATH=.pythontest/test_env.pyCurrent StatusDQN basically solves it after 100k steps.Normalized the easy way using vecnormalize.Added a zeroth action and trimmed the action space a bitTODOcomplicate the problem! multiple crops, and they need to start dieing off at some pointmake env realistic -- add different plantsfix planting densityadd different plants which have different maturities, weather needs etc.plus weather forecast, soil quality(split into attributes)
Package: farmyard
🌾 Farmyard 🐄The Minimal Reinforcement Learning FrameworkContributionsare welcome!Table Of ContentsOverviewRoadmapOverviewThe minimal reinforcement learning framework.Get started withfarmyardby installing it with $pip install farmyardor cloning this repository.RoadmapThis project is being built while I learn about reinforcement
learning, the roadmap for the project includes:Common RL Algorithms (Easily Comparable)Common RL Performance Tricks (Separate from Algorithms)Common RL ModelsFlexible/Modular ComponentsMultiprocessingDistributed Computing
Package: farneback3d
A CUDA implementation of the Farneback optical flow algorithm[1]for the calculation of dense volumetric flow fields. Since this algorithm is based on the approximation of the signal by polynomial expansion it is especial suited for the motion estimation in smooth signals without clear edges.To know more about the implementation, have a look onthis OpenCV classthat was used as a template for this implementation.Development takes place onGithub.Python interfaceThe project usespycudato provide a pure-python package available on PyPipipinstallfarneback3dUsage:importfarneback3d...# create some numpy volumes vol0 and vol1 (can also be pycuda GPUArrays)# set parameters for optical flow (parameters have the same meaning as in the OpenCV implementation)optflow=farneback3d.Farneback(pyr_scale=0.8,# Scaling between multi-scale pyramid levelslevels=6,# Number of multi-scale levelsnum_iterations=5,# Iterations on each multi-scale levelwinsize=9,# Window size for Gaussian filtering of polynomial coefficientspoly_n=5,# Size of window for weighted least-square estimation of polynomial coefficientspoly_sigma=1.2,# Sigma for Gaussian weighting of least-square estimation of polynomial coefficients)# calculate frame-to-frame flow between vol0 and vol1flow=optflow.calc_flow(vol0,vol1)C++ interfaceTo be implemented…Future plansThe current implementation uses a naive approach to perform the necessary convolutions.
The algorithm could be sped up drastically by performing separable convolutions along each coordinate axis.References[1]Farnebäck, Gunnar. “Two-frame motion estimation based on polynomial expansion.” Scandinavian conference on Image analysis. Springer, Berlin, Heidelberg, 2003.
Package: faro
Overviewfarois a fast, simple, and intuitive SQL-driven data analysis library for Python. It is built on top ofsqliteand is intended to complement the existing data analysis packages in the Python eco-system, such asnumpy,pandas, andmatplotlibby providing easy interoperability between them. It also integrates with Jupyter by default to provide readable and interactive displays of queries and tables.UsageCreate aDatabaseobject and give it a name.fromfaroimportDatabasedb=Database('transportation')To add tables to the in-memory database, simply specify the name of the file. Supported file types include:csv,json, andxlsx.add_tableinserts the contents of a file into a new table within the database. It can automatically detect the filetype and parse the file contents accordingly. In this example we load two different tables, one incsvformat, and the other injsonformat.db.add_table('cars.json',name='cars')db.add_table('airports.csv',name='airports')We can also directly passpandas.DataFrameorfaro.Tableobjects to be added to the database. A helpful pattern when dealing with more complex parsing for a specific file is to read it into memory usingpandasthen add theDataFrameto thefaro.Database.buses=pd.DataFrame({'id':[1,2,3,4,5],'from':['Houston','Atlanta','Chicago','Boston','New York'],'to':['San Antonio','Charlotte','Milwaukee','Cape Cod','Buffalo']})db.add_table(buses,name='buses')Alternatively, we can directly assign to a table name as a property of thetableobject. Using this method, however, will also replace the entire table as opposed to the options offered byadd_table()db.table.buses=busesWe can now query against any table in the database using pure SQL, and easily interact with the results in a Jupyter Notebook.sql="""SELECT iata,name,city,stateFROM airportsWHERE country = 'USA'LIMIT 5"""db.query(sql)iatanamecitystate000MThigpenBay SpringsMS100RLivingston MunicipalLivingstonTX200VMeadow LakeColorado SpringsCO301GPerry-WarsawPerryNY401JHilliard AirparkHilliardFLIf we want to interact with the data returned by the query, we can easily transform it into whatever data type is most convenient for the situation. Supported type conversions include:List[Tuple],Dict[List],numpy.ndarray, andpandas.DataFrame.table=db.query(sql)type(table)>>>faro.table.Tabledf=table.to_dataframe()type(df)>>>pandas.core.frame.DataFramematrix=table.to_numpy()type(matrix)>>>numpy.ndarrayWe can also interact with the tables in our database by accessing them as properties of thetableobject. For example:db.table.busesidfromto11HoustonSan Antonio22AtlantaCharlotte33ChicagoMilwaukee44BostonCape Cod55New YorkBuffalo
Package: faros
Faros Python ClientPython client for theFarosAPIDocumentationPlease read theFaros documentationto learn more. See also ourapps repofor examples to help you get started.Developing$pipinstall-rrequirements.txtWe currently using flake8 for linting so ensure your code editor is using that
for linting. You can also runflake8 . --count --show-source --max-complexity=10 --max-line-length=120 --statistics:memo: NOTE: Note: In the future will switch to something likeblackto automatically format on commit.:memo: If you add new packages to the client, also add them toinstall_requiressection insetup.py, to ensure their added to packaged client published toPyPiReleasingTo release a new version of the client first bump the version ininit file.From the repo's home page, click on "releases" then "Draft a new release".In the "Tag version" input, select the tag corresponding to the version you want to release from the auto-complete or type in to create a new versionEnter a release title and description, check the pre-release box if appropriate, and click "Publish release".ThePublish to PyPiworkflow in this repository will then automatically
publish the code to PyPi. If all goes well, you be able to see it shortly
afterherewith the new version.
Package: farrow-and-ball
Farrow&Ball MatplotlibThis is a python port for the matplotlib library of theR Packagefor ggplot2.The style for the charts is provided in thestylesfolderInstallationA python version >= 3 is required.pipinstallfarrow-and-ballUsageThe usage is quite simple:fromfarrow_and_ballimport*importnumpyasnpimportmatplotlib.pyplotasplt# Define a gray scale imagex=np.arange(0,np.pi,0.1)y=np.arange(0,2*np.pi,0.1)X,Y=np.meshgrid(x,y)Z=np.cos(X)*np.sin(Y)*10# Get the color mapcmap=build_colormap(DivergentPalette.DAY,True)# Draw imageplt.imshow(Z,origin="lower",cmap=cmap)plt.show()One can also directly get the color map definition as a list of strings withfarrow_and_ball.get_palette(DivergentPalette.DAY)or an interpolated one withfarrow_and_ball.get_interpolated_palette(DivergentPalette.DAY, num_classes).PalettesThe palettes are organized in Enums:classSpectralPalette(Enum):DEEPSPEC="deepspec"SPEC="spec"LIGHTSPEC="lightspec"LIGHTERSPEC="lighterspec"classDivergentPalette(Enum):DAY="day"DAYLONG="daylong"ARMY="army"MONO="mono"classBaseColorPalette(Enum):PINKS="pinks"PINKS_VAR="pinks2"REDS="reds"YELLOWS="yellows"GREENS="greens"GREENS_VAR="greens2"BLUES="blues"BLUES_VAR="blues2"classMiscPalette(Enum):TONKA="tonka"BELLSPOUT="bellsprout"DOCKERS="dockers"FRUITYPEBBLES="fruitypebbles"One can also directly enter the name as a string, e.g.build_colormap("day", True).Color MapsContinuousDiscrete