Saravanan666/pypi-org huggingface dataset:
Package: ingatesdk
Ingate Python SDKOverviewThe Ingate Python SDK provides an interface to configure and manage your Ingate
appliances (including upgrades, licenses and patches) via HTTP requests.ExamplesChange the unit nameChange the name of the unit toTestapi. Create aClientinstance. Request a
security token. Call the methodmodify_single_rowin order to change the name
and print the result. Store the change to the permanent configuration.import sys

from ingate import ingatesdk

scheme = 'http'
user = 'alice'
password = 'foobar'
address = '192.168.1.1'
port = ''

try:
    # Create API client.
    api_client = ingatesdk.Client('v1', scheme, address, user, password,
                                  port=port)

    # Authenticate and get hold of a security token.
    response = api_client.authenticate()

    # Change the unit name.
    unitname = 'Testapi'
    response = api_client.modify_single_row('misc.unitname', unitname=unitname)
    print('Changed the unit name to %s' % (response[0]['data']['unitname']))
    print('')

    # Store the preliminary configuration to the permanent configuration.
    response = api_client.store_edit()
    print(response[0]['store-edit']['msg'])
    print('')

except Exception as e:
    sys.stderr.write('\n%s: %s\n' % (type(e).__name__, str(e)))Apply a base license and upgrade to latest firmware versionimport sys

from ingate import ingatesdk

scheme = 'http'
user = 'alice'
password = 'foobar'
address = '192.168.1.1'
port = ''

try:
    # Create API client.
    api_client = ingatesdk.Client('v1', scheme, address, user, password,
                                  port=port)

    # Authenticate and get hold of a security token.
    response = api_client.authenticate()

    # Install a license.
    response = api_client.install_license('myaccount', 'mypassword',
                                          'JJV8-9JVT-BV36')

    # Store the edit configuration. The 'download_install_upgrade' function
    # expects that the configuration has been stored at least once.
    api_client.store_edit()

    # Upgrade to the latest version available.
    response = api_client.download_install_upgrade('myaccount', 'mypassword',
                                                   latest=True)

except Exception as e:
    sys.stderr.write('\n%s: %s\n' % (type(e).__name__, str(e)))Additional informationMore information and examplesGenerate Python code from an Ingate CLI backup fileGenerate Ansible Playbook from an Ingate CLI backup file
Package: ingenialink
ingenialink-python is a Python library for simple motion control tasks and communication with Ingenia drives.RequirementsPython 3.8 or higherWinPcap4.1.3DevelopmentIn case that library will be use from source code the FoE application will be missing.
It must be added toingenialink/bin/FoE/win_64xin order to use FOE feature.InstallationInstallation is done by usingpip, i.e:pip install ingenialinkDocumentationFor further details you can read thedocumentationwhere you will find
simple usage examples, the API docs, etc.
Package: ingenialogger
IngenialoggerModule to unify libraries, script and programs logs.How to use itModule and librariesimportingenialoggerlogger=ingenialogger.get_logger(__name__)logger.info("Info log",axis=1,drive="EVE-XCR (192.168.2.22)")logger.user_warning("User Warning log",code_error="0x2342")Final applicationimportingenialoggerlog_queue=ingenialogger.configure_logger(level=ingenialogger.LoggingLevel.INFO,queue=True)logger=ingenialogger.get_logger(__name__)logger.user_info("User Info log",axis=2,drive="My drive")log_msg=log_queue.get().msgprint(ingenialogger.LogParser.parse_log(log_msg))
Package: ingeniamotion
Ingeniamotion is a library that works over ingenialink and aims to simplify the interaction with Ingenia’s drives.How it works?All ingeniamotion functionalities works through the MotionController class. So, first of all we should
instantiate a MotionController object.fromingeniamotionimportMotionControllermc=MotionController()Now,mcis our MotionController instance.Then, we should connect some servos.# In case we want to connect only one servomc.communication.connect_servo_eoe("192.168.2.22","eve-net_1.7.0.xdf")# In case we want to connect more servosmc.communication.connect_servo_eoe("192.168.2.22","eve-net_1.7.0.xdf",alias="servo_one")mc.communication.connect_servo_eoe("192.168.2.23","eve-net_1.7.0.xdf",alias="servo_two")# The "alias" field will allow to reference these servos in the future.# The "alias" can be whatever we want to use as identifier.Now, the servos are ready and we can work with them.We then can apply some configurations:# If we have only one servomc.configuration.release_brake()# By default it uses the axis 1Or we can execute some tests or calibrations:mc.tests.digital_halls_test(servo="servo_one",axis=1)mc.tests.commutation(servo="servo_two",axis=1)MotionController namespacesMotionController functionalities are group in the following namespaces.CommunicationThis namespace has all the basic communication functions with the servo:
connect, read or write a register, load firmware, etc.ConfigurationHere we will find functions to configure the servo:
load or save configuration, configure limits, feedbacks, brake settings, etc.MotionIn this namespace we will find all the functions that will help us to move the servos.CaptureThis namespace will help us to work with monitoring and similar features.InfoFunctions to get register information from dictionary.ErrorsNamespace to manage drive errors and get errors data.TestsThe functions of this namespace will help us to lunch some tests for the commissioning process.Common exceptionsKeyError:"Servo 'default' is not connected"ThisKeyErrorexception is raised whenever we use a function that interacts with the drive but no drive is connected.TypeError:'NoneType'objectisnotsubscriptableThisTypeErrorexception is raised when we provide a function with the wrong servo axis number.ingenialink.exceptions.ILErrorThisingenialink.exceptions.ILErrorexception is raised when the drive gets abruptly disconnected.
Package: ingeniiadfg
Ingenii Azure Data Factory GeneratorPython based generator to create Azure Data Factory pipelines from configurations.This package integrates easily with theIngenii Azure Data Platform, but this package can be used independently as long as some resouces are created ahead of time. These are detailed in theUsage documentation.Current Version: 0.1.5Package installationInstall the packageusing pipwithpip install ingeniiadfgor, for a particular versionpip install ingeniiadfg==0.1.5Alternatively, add it to your repository's currentrequirements.txtfile.Using the packageFor details on using the package please refer to theAzure Data Factory Usage documentation.Example CI/CDFor deploying into a Data Factory that is not integrated with a repository, also known as 'live' mode, we have included some example CI/CD pipelines in theCICDfolder. These are in the format to be read by Azure Pipelines. Feel free to use these yourself or for inspiration in creating your own pipelines.Version History0.1.5: Move to a CLI package, and add several fixes0.1.4: Add object annotations to track what is managed by this package0.1.3: Extend schedule to handle when only the hours of the dayt are specified0.1.2: Change the name of the secret name for the SAS URI to access the config tables0.1.1: Add schedule generation from configuration, many more tests0.1.0: Initial package, FTP/SFTP connections with basic authentication
Package: ingenii-azure-data-platform
Ingenii Azure Data Platform Helper PackageDetailsCurrent Version: 0.0.6OverviewThis python package has config loaders, schema validators and helper functions needed by the Azure Data Platform Pulumi solution.Usagepipinstallingenii-azure-data-platformPyPi Project Pagehttps://pypi.org/project/ingenii-azure-data-platformReleasing A New VersionThere is a GitHub workflow (release.yml) that triggers on any GitHub releases.Make sure to update the package version in README.mdotherwise PyPi will reject the upload, if the same version is already uploaded.TODOSImprove the documentation for this repoAdd readme file for the python packageAdd testsAdd makefile for easier interactionAdd a build/test only GitHub workflow
Package: ingeniictl
ingeniictl - Ingenii's Swiss Army Knifeingeniictl - Ingenii's Swiss Army KnifeOverviewDevelopmentMakefilesReleasing New VersionInstallEnvironment VariablesExecutableCommandsOptionsOverviewWe have been using Makefiles to help us augument Pulumi with pre/post deployment automation. The goal of this CLI is not to fully replace the Makefiles and the countless targets in there, but to greatly reduce their size.DevelopmentLaunch the Visual Studio CodeOpen the project in Dev ContainerCongratulations. You have all necessary tools to extend this CLI.MakefilesThere are some handy shortcuts in the makefile.make install- Installs all dependenciesmake build- Builds the ingeniictl and outputs thewhlandzipfiles in thedistdir.make publish TOKEN=<pypi token>- Builds and publishes the ingeniictl to pypi.make publish-test TOKEN=<pypi token>- Builds and publishes the ingeniictl to the test pypi.make test- Runs tests.Releasing New VersionMake your changesTest locallyBump the package version:poetry version <patch | minor | major | prepatch | preminor | premajor | rerelease>Open a Pull Request (Merge to Releases)Get someone to review and mergeThe CI will automatically publish the new versionInstallpip install ingeniictlEnvironment VariablesII_LOG_ENABLE_COLORS- Set to0to disable colors in the output messages.II_LOG_ENABLE_DATETIME_PREFIX- Set to0to disable the date/time prefix in the output messages.ExecutableingeniictlCommandsinfra- Infrastructure ToolkitOptions--install-completion[bash|zsh|fish|powershell|pwsh]Installcompletionforthespecifiedshell.--show-completion[bash|zsh|fish|powershell|pwsh]Showcompletionforthespecifiedshell,tocopyitorcustomizetheinstallation.--help
Package: ingenii-quantum-hybrid-networks
Ingenii Quantum Hybrid NetworksVersion: 0.2.0Package of tools to integrate hybrid quantum-classical neural networks to your machine learning algorithms.
The algorithms provided in this package are implemented both in Qiskit (meant to run on real hardware and fake providers) and in Pytorch or Tensorflow (meant to run in quantum simulation with CPUs or GPUs). This package contains the following quantum algorithms:Quantum convolutional layer (2D and 3D):It is designed to reduce the complexity of the classical 2D/3D CNN, while maintaining its prediction performance. The hybrid CNN replaces a convolutional layer with a quantum convolutional layer. That is, each classical convolutional filter is replaced by a quantum circuit, which acts as a quantum filter. Each quantum circuit is divided into two blocks: the data encoding, which maps the input data into a quantum circuit, and the quantum transformation, where quantum operations are applied to retrieve information from the encoded data. Tha package contains an implementation for 2D data (images) and for 3D data (volumes).Quantum fully-connected layerIt is designed to construct hybrid quantum-classical neural networks, where the quantum layer is a fully-connected layer. The quantum layer is a parametrized quantum circuit, composed of three parts: a data encoding which maps the classical data into a quantum circuit, a parametrized quantum circuit, which performs quantum operations with parameters, and measurements, which produce the output of the layer. Multiple quantum architectures are provided, which have been extracted from previous studies of hybrid neural networks.Update:To improve efficiency of the training, the codes have been rewritten using Pennylane instead of Qiskit. You can still run the algorithm in Qiskit devices by providing the suitable backend name.Quantum fusion modelIt is designed to efficiently integrate the extracted features from two classical neural network models to produce enhanced predictions. The proposed model strategically integrates 3D-CNNs and SG-CNNs to leverage their respective strengths in processing diverse facets of the training data.
The simulation results presented here will demonstrate the superior performance of the quantum fusion model relative to state-of-the-art classical models.Quantum Hadamard Edge Detection (2D and 3D):Performs edge detection for 2D data (images) and 3D data (volumes), using quantum operations.
Package: ingescape
Ingescape Python bindingDependenciesThe Ingescape Python binding relies on the following libraries:Python 3.6+setuptools, wheelInstallingIngescape python binding is designed to be installed through pip :python3 -m pip install ingescapeUsing Ingescape with PythonTwo examples are provided in the 'examples' directory.Compiling the Ingescape Python bindingIngescape library must be built before compiling the python binding.
The directory in which the library is expected to be built is given by the prepare scripts..
Run the following commands from the Ingescape Python directory :python3 -m pip install setuptools wheel
./prepare_for_packaging.sh (or prepare_for_packaging.ps1 for Windows Powershell)
# Build package 
python3 -m build
# Install locally
python3 -m pip install .
Package: ingester3
Ingester3Ingester3 is the Pandas extension-based system for ingesting data in the ViEWS3 system.FundingThe contents of this repository is the outcome of projects that have received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 694640,ViEWS) and Horizon Europe (Grant agreement No. 101055176,ANTICIPATE; and No. 101069312,ViEWS(ERC-2022-POC1)), Riksbankens Jubileumsfond (Grant agreement No. M21-0002,Societies at Risk), Uppsala University, Peace Research Institute Oslo, the United Nations Economic and Social Commission for Western Asia (ViEWS-ESCWA), the United Kingdom Foreign, Commonwealth & Development Office (GSRA –Forecasting Fatalities in Armed Conflict), the Swedish Research Council (DEMSCORE), the Swedish Foundation for Strategic Environmental Research (MISTRA Geopolitics), the Norwegian MFA (Conflict TrendsQZA-18/0227), and the United Nations High Commissioner for Refugees (the Sahel Predictive Analytics project).
Package: ingestion
clarolib - Evoluindo a Biblioteca de validaÃ§Ã£o de dadosPrÃ© RequisitosPython 3.xDocker e Docker ComposeIntroduÃ§Ã£oO objetivo desse documento Ã© descrever os passos para criaÃ§Ã£o do ambiente de desenvolvimento da bibliotecaClarolib.Essa biblioteca foi criada com os seguintes objetivos:Padronizar a etapa de validaÃ§Ã£o das fontes de dados a serem ingeridas noDatalakePadronizar mÃ©tricas e implementaÃ§Ãµes delinhagem de dadosDar mais agilidade/produtividade na criaÃ§Ã£o de esteiras de validaÃ§Ãµes de dados antes que esses sejam ingeridos noDatalakeEvitar necessidade de repetiÃ§Ã£o de cÃ³digos e deixar o cÃ³digo 'mais limpo'.Abaixo estÃ£o os passos para criaÃ§Ã£o do ambiente de desenvolvimento:CriaÃ§Ã£o deAmbiente Virtual PythonPara criar o ambiente virtual, execute os comandos abaixo:python3-mvenvclarolib_envsourceclarolib_env/bin/activate

pipinstall-rrequirements.txtExecutando o Apache Spark localmente com DockerAcesse a pastasparkfolder no seu terminal e execute o comando abaixo.
Esse serÃ¡ responsÃ¡vel por iniciar a execuÃ§Ã£o do spark localmente.docker-composeupComo executar exemplos localmenteAcesse os arquivos de exemplo localizados no diretÃ³riocode_examplese execute-os.Desativando o ambiente virtualAcesse o diretorioclarolib_enve execute o seguinte comando:deactivateCompilando e executando testes unitÃ¡riosPara compilar os cÃ³digos, disparar a execuÃ§Ã£o dos testes unitÃ¡rios e validaÃ§Ã£o decode lintexecute os passos conforme abaixo:flake8./lib./code_examples./tests
pytest--ignore=setup.py
python3setup.pysdistbdist_wheelEsses mesmos passos serÃ£o executados pela esteira do Azure Devops para garantir que os critÃ©rios mÃ­nimos de qualidade estÃ£o sendo seguidos.Caso o cÃ³digo criado nÃ£o esteja de acordo, a esteiranÃ£opermitirÃ¡ que esse cÃ³digo seja entregue.
Package: ingestion-game
The Ingestion GameThe Ingestion Game consists of:A data file with a varying number of entries containing some data. It is shaped as a CSV where each row has
an arbitrary number ofkey=valuepairs separated by comma.key1=value1,key2=value2
key3=value3
key4=value4,key5=value5,key6=value6Not allkeys are always important! Each time we play the game, the keys to process can be different. The rest
are just considered noise. We want to ingest all the important keys.All important keys are always going to be present in the data rows of the input file.Eachkeysupports a specific data type. We should only process rows where the values match the expected datatype of each key.Each row consists of an Entity of a given type. The type will be informed astype=Tin the data.typeis
always considered as an important key and we can assume it is astr.Entities have hierarchies defined in the games' rules. For example, given the ruleA -> B, we cannot
process any Entity of typeBuntil we have processed all Entities of typeA.Your task is to win the Ingestion Game with the following rules:The important keys areid:int,name:str,food:strandtype:str.Entities follow the hierarchyA -> B -> C.The solution must be coded in Python and you can use any public libraries. The solution must be an CLI that outputs
the processed data tostdoutin CSV format following the important keys, e.g.,:id,name,food,type
1,levy,veggies,A
2,lima,pizza,A
3,john,fish,BWe are going to provide a data file with the keys and hierarchies mentioned above, but your CLI should accept any
important keys and hierarchies as inputs. You can see an example on input data in the providedinput.txt.🤓 We value in the solutionGood software designProper documentationCompliance to Python standards and modern usages (e.g.:PEP8)Proper use of data structuresErgonomy of the command line interfaceSetup/Launch instructions if requiredSolutionInstall using pip:pipinstallingestion-gameRunHelp:ingestion_game--helpParameters:IMPORTANT_KEYS: [required] separate by commas without spaces
example:id:int,name:str,food:str,type:strHIERARCHY: [required] separate by commas without spaces
example:A,B,CPATH: [required] path of the input file
example:/files/input.txtExample:ingestion_gameid:int,name:str,type:strA,B,C/file/input.txt
Package: ingestr
Ingest & copy data from any source to any destination without any codeIngestr is a command-line application that allows you to ingest data from any source into any destination using simple command-line flags, no code necessary.✨ copy data from your database into any destination➕ incremental loading:append,mergeordelete+insert🐍 single-command installationingestr takes away the complexity of managing any backend or writing any code for ingesting data, simply run the command and watch the data land on its destination.Installationpip install ingestrQuickstartingestringest\--source-uri'postgresql://admin:admin@localhost:8837/web?sslmode=disable'\--source-table'public.some_data'\--dest-uri'bigquery://<your-project-name>?credentials_path=/path/to/service/account.json'\--dest-table'ingestr.some_data'That's it.This command will:get the tablepublic.some_datafrom the Postgres instance.upload this data to your BigQuery warehouse under the schemaingestrand tablesome_data.DocumentationYou can see the full documentationhere.CommunityJoin our Slack communityhere.Supported Sources & DestinationsDatabaseSourceDestinationPostgres✅✅BigQuery✅✅Snowflake✅✅Redshift✅✅Databricks✅✅DuckDB✅✅Microsoft SQL Server✅✅Local CSV file✅✅Oracle✅❌SQLite✅❌MySQL✅❌More to come soon!AcknowledgementsThis project would not have been possible without the amazing work done by theSQLAlchemyanddltteams. We relied on their work to connect to various sources and destinations, and builtingestras a simple, opinionated wrapper around their work.
Package: ingesture
ingestureIngest gesturally-structured data into models with multiple exportThis package isnoteven close to usable, and is just a sketch at the moment.
If for some reason you see it and would like to work on it with me, feel free to
open an issue :)Declare your dataEven the most disorganized data system hassomestructure. We want to be able
to recover it without demanding that the entire acquisition process be reworkedTo do that, we can use a family of specifiers to tellingestwhere to get metadatafromdatetimeimportdatetimefromingestureimportSchema,specfrompydanticimportFieldclassMyData(Schema):# parse metadata in a filenamesubject_id:str=Field(...,description="The ID of a subject of course!",spec=spec.Path('electrophysiology_{subject_id}_*.csv'))# parse multiple values at oncedate:datetimeexperimenter:strdate,experimenter=Field(...,spec=spec.Path('{date}_{experimenter}_optodata.h5'))# from inside a .mat fileother_meta:int=Field(...spec=spec.Mat(path='**/notebook.mat',# 2 **s mean we can glob recursivelyfield=('nb',1,'user')# index recursively through the .mat))# and so onThen, parse your schema from a folderdata=MyData.make('/home/lab/my_data')Or a bunch of them!data=MyData.make('/home/lab/my_datas/*')Multiple StrategiestodoHierarchical ModelingOur data is rarely a single type, often there is a repeatable substructure that
is paired with different macro-structures: eg. you have open-ephys data within a directory
with behavioral data in one experiment and paired with optical data in another.Make submodels and recombine them freely...todoExport DataOnce we have data in an abstract model, then we want to be able to export it to
multiple formats! To do that we need an interface that describes
the basic methods of interacting with that format (eg. .csv files are
written differently than hdf5 files) and a mapping from our model fields
to locations, attributes, and names in the target format.Pydantic base exportjsonFrom the Field specificationclassMyData(Schema):subject_id:str=Field(spec=...,nwb_field="NWBFile:subject_id")From aMappingobjectclassNWB_Map(Mapping):subject_id='NWBFile:subject_id'classMyData(Schema):subject_id:str=Field(...)__mapping__=NWB_Map
Package: ingi-cli
ingi-cliA CLI tool for gists like no other. Run, download and help build a searchable gist APIFeaturesRun gists directly from your bash. Multiple language support (python, ruby, shell, node)Offline cache for gistsInstallationingiassumes that any required binaries for executing gists are installed on your system, if not then it will fail.Install the cli tool through pip:pip install ingi-cliUsageInvoking a gistingi get https://api.github.com/gists/ac5a0fb49370e7cff3bee93ecc40b9a8or use the gist IDingi get --use_id ac5a0fb49370e7cff3bee93ecc40b9a8You can also just cache the gist to use lateringi get --save_only --use_id ac5a0fb49370e7cff3bee93ecc40b9a8TODO & ImprovementsImplementing a gist index to search gists programmatically. Any gists executed locally get automatically inserted and indexed on API to be used.
This will provide us with a rest API to search for gists 😀.Configurable invoke queue, like aninvoke.yamlwhich lists a bunch of gists to execute in order.arguments to be passed to scripts, for e.g:ingi get --use_id ac5a0fb49370e7cff3bee93ecc40b9a8 first_arg second_arg