Saravanan666/pypi-org huggingface dataset:
Package: imp-prof
imp-profImp-prof serves to collect and publishprometheusmetrics from pre-forked uWSGI server workers.flowchart LR
    A0W0(app0-worker0)
    A0W1(app0-worker1)
    A1W0(app1-worker0)
    A1W1(app1-worker1)
    E(profiling):::exchange
    Q(profiling_queue):::queue
    I(pika consumer)
    F(fast api)
    P(prometheus)
    subgraph source[server app 0]
        A0W0
        A0W1
    end
    subgraph source2[server app 1]
        A1W0
        A1W1
    end
    A0W0-->|push profile array|E
    A0W1-->|push profile array|E
    A1W0-->|push profile array|E
    A1W1-->|push profile array|E
    subgraph rabbit[rabbit MQ]
        E-->|fanout|Q
    end
    subgraph imp[imp-prof]
        I-->|write profile|F
    end
    Q-->|fetched event|I
    Q-.-|consume|I
    P-->|fetch /metrics|F
    classDef exchange fill:#A08565;
    classDef queue fill:#619A46;
Package: im-present
im-presentSimple utility which moves your mouse by 1 pixel and then back, at a specified interval.  Useful for pretending to be at your computer and fooling the apps which track your productivity.Installationpipinstallim-presentUsageusage: __main__.py [-h] [-i secs] [-j secs] [-t secs]

periodically shakes the mouse pointer to prove you're here

optional arguments:
  -h, --help  show this help message and exit
  -i secs     number of seconds to wait in between mouse movements. Defaults to 600.
  -j secs     number of seconds to wait in between mouse jitters. Defaults to 0.5.
  -t secs     if set, exit after this many seconds have elapsed from the time this program was first started.
Package: impressionismcat
😸😹😺😻😼😽😾😿🙀🐱ImpressionismCat is a deep learning project that applies most cutting edge techniques to make art. We believe that a better understanding of how deep learning works contributes to the understanding of ourselves as human beings.ImpressionismCat has realized the following algorithms:Neural style transfer (Gatys, Ecker, and Bethge, “A Neural Algorithm of Artistic Style.”).TBAStory of the ImpressionismCatDavid, a cat of my friend Selia, is the ambassador of the project.Selia and me were co-workers at IBM and we met in a strategy project in the autum of Beijing in 2020. Since then I know Selia got a incredible cute cat. So when the project came into shape and needed an ambassador, her cute cat just came across my mind and I know that’s it!LicenseGossipCat is licensed under the MIT License. © Contributors, 2022.A permissive license whose main conditions require preservation of copyright and license notices. Contributors provide an express grant of patent rights. Licensed works, modifications, and larger works may be distributed under different terms and without source code.
Package: impressive-strawberry
Impressive StrawberryAchievements-as-a-serviceAboutImpressive Strawberry is a Web API that allows the consumer applications to manage user-unlockable achievements, and for external applications to unlock them
for the users, notifying them in the process.DevelopmentDesign documentThe design document was madeon FigJam.DocumentationDocumentation is made availablevia GitHub Pages.TasksTasks to be done are publishedon GitHub Issues.If youfind a bug, amistake, or want tosuggest a new feature,
pleasecreate a new issue!Pull requestsFeel free to fork the project and continue developing it, but remember torespect the AGPL 3.0+ license!If you want to contribute back to the main branch, pleaseopen a new pull request!DiscussionA forum about the project is availableon GitHub Discussions.If you want totalk about the project, orask a questionor evenhelp,
pleasecreate a new discussion!CI / CDCI and CD for this project is hostedon GitHub Actions.JobsTestsare runon every pushandon every pull request.CodeQLis runevery monday at 10:10 AM.Build and publishare runon every release.All jobs can be triggered simultaneously by pressing theRun Workflowbuttonon the Workflow page.RoadmapThe roadmap is publishedon GitHub Projects.SecurityPlease report security vulnerabilities via email atsecurity@steffo.eu.
Package: impresso-commons
impresso_pycommonsPython module with bits of code (objects, functions) highly-reusable within theimpresso project.Please refer to thedocumentationfor further information on this library.InstallationWithpip:pipinstallimpresso-commonsLicenseThe second project 'impresso - Media Monitoring of the Past II. Beyond Borders: Connecting Historical Newspapers and Radio' is funded by the Swiss National Science Foundation (SNSF) under grant numberCRSII5_213585and the Luxembourg National Research Fund under grant No. 17498891.Aiming to develop and consolidate tools to process and explore large-scale collections of historical newspapers and radio archives, and to study the impact of this tooling on historical research practices,Impresso IIbuilds upon the first project – 'impresso - Media Monitoring of the Past' (grant numberCRSII5_173719, Sinergia program). More information athttps://impresso-project.ch.Copyright (C) 2024  Theimpressoteam (contributors to this program: Matteo Romanello, Maud Ehrmann, Alex Flückinger, Edoardo Tarek Hölzl, Pauline Conti).This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of merchantability or fitness for a particular purpose. See theGNU Affero General Public Licensefor more details.
Package: impresso-pycommons
impresso_pycommonsPython module with bits of code (objects, functions) highly-reusable within theimpresso project.Please refer to thedocumentationfor further information on this library.InstallationWithpip:pipinstallimpresso_pycommonsLicenseThe 'impresso - Media Monitoring of the Past' project is funded by the Swiss National Science Foundation (SNSF) under  grant numberCRSII5_173719(Sinergia program). The project aims at developing tools to process and explore large-scale collections of historical newspapers, and at studying the impact of this new tooling on historical research practices. More information athttps://impresso-project.ch.Copyright (C) 2020  Theimpressoteam (contributors to this program: Matteo Romanello, Maud Ehrmann, Alex Flückinger, Edoardo Tarek Hölzl).This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of merchantability or fitness for a particular purpose. See theGNU Affero General Public Licensefor more details.
Package: impresso-text-importer
Impresso Text ImporterThe Impresso TextImporter is a library and a collection of scripts to import newspaper data from a variety of formats (e.g. Olive XML, various flavors of Mets/Alto XML, etc.) intoImpresso’s JSON format.Please refer to thedocumentationfor further information on this library.InstallationWithpip:pipinstallimpresso-text-importerLicenseThe second project 'impresso - Media Monitoring of the Past II. Beyond Borders: Connecting Historical Newspapers and Radio' is funded by the Swiss National Science Foundation (SNSF) under grant numberCRSII5_213585and the Luxembourg National Research Fund under grant No. 17498891.Aiming to develop and consolidate tools to process and explore large-scale collections of historical newspapers and radio archives, and to study the impact of this tooling on historical research practices,Impresso IIbuilds upon the first project – 'impresso - Media Monitoring of the Past' (grant numberCRSII5_173719, Sinergia program). More information athttps://impresso-project.ch.Copyright (C) 2023  Theimpressoteam (contributors to this program: Matteo Romanello, Maud Ehrmann, Alex Flückinger, Edoardo Tarek Hölzl, Pauline Conti).This program is free software: you can redistribute it and/or modify it under the terms of the GNU Affero General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of merchantability or fitness for a particular purpose. See theGNU Affero General Public Licensefor more details.
Package: imprint
imprintThis program is a system for data aggregation and presentation through Microsoft
Word documents. It uses templates to define dynamically generated portions of
the document, which the user can construct in arbitrary ways. The content of the
document is separated from the style information and the content generation.Using this system does not remove the need to write the initial draft of the
document you want to create, or the need to generate the data you plan on
aggregating. This program is only useful if you plan on generating a similarly
formatted document many times with the same content but different data sources.
It is not useful for one-off reports or documents.See the documentation athttps://imprint.readthedocs.io/en/latestfor more
information, including the history of the project, turorials, and API
references and installation instructions.The source and binaries for this program are available on PyPI. The simplest
installation method is withpip:pip install imprint[all]The extra[all]is recommended for but not required. Installing from source
is also an option, by runningpython setup.py installin the root folder of the project source distribution. The source distribution,
including documentation can be found athttps://github.com/madphysicist/imprint.This program in licensed under the AGPLv3 or later. You should have received a
copy of the GNU Affero General Public License along with this program. If not,
seehttps://www.gnu.org/licenses/.Changelog0.1.0a3 (2023-03-26)Migrated hosting to GitLab0.1.0a2 (2022-01-20)Documentation updatesSource distribution includes docs0.1.0a1 (2019-04-01)Added new haggis optional dependencies0.1a1.dev2 (2019-03-26)Added majority of documentation0.1a1.dev1 (2019-03-20)First release on PyPI.Created Read the Docs pages.Code 95% complete.Documentation 50% complete (reference portion 95% complete).No tests worth speaking of.
Package: improb
The library supports arbitrary finitely generated conditional lower
previsions, belief functions, linear-vacuous mixtures, probability
measures, n-monotone lower probabilities, Mobius transforms, and
Choquet integration.Various decision criteria, such as Gamma-maximin, Gamma-maximax,
interval dominance, and maximality, are implemented. For sequential
decision problems, the library has a convenient interface for
constructing decision trees of any size, and has algorithms for
solving them by normal form, or by normal form backward induction.Download:http://pypi.python.org/pypi/improb/#downloadsDocumentation:http://packages.python.org/improb/Development:http://github.com/mcmtroffaes/improb/
Package: im-protobuf
Informatics Matters cross-product protocol buffersA library of python bindings forprotocol bufferdefinitions used by one or
more products in the Informatics Matters product suite.The protocol buffers are used across multiple components and languages.
At the outset we anticipate supporting Python, and Java. The root
of all packages issrc/mainas required by build tools likeGradle.
From there the directory isproto/informaticsmattersfollowed by component
directories or acommondirectory. An example protocol message
file might be:src/main/proto/informaticsmatters/protobuf/datamanager/pod_message.protoWhen transmitted on a topic-based messaging service the topic is
the lower-case dot-separated message name relative toinformaticsmatters(excluding theMessagesuffix), e.g.datamanager.pod.Installation (Python)The protocol buffers are published onPyPIand can be installed from
there:pip install im-protobufOnce installed you can access the protocol buffers with:>>> from informaticsmatters.protobuf.datamanager.pod_message_pb2 import PodMessage
>>> pm: PodMessage = PodMessage()Get in touchReport bugs, suggest features or view the source codeon GitHub.
Package: improutils
This repository contains Python library with useful helper functions for Machine vision and Image processing (BI-SVZ) coursework taught atFIT CTU. For more details, seemain course page.How to deploy a new versionThis repository uses GitLab CI/CD Pipelines to deploy improutils package either to production or test PyPI.Steps to release a new version to production PyPIUpdate version.py file with a new version number with respect tosemantic versioning rulesCommit your local changesgit commit -m "Add awesome AI feature"Create a tagged version based on version.pygit tag -a $(python3 setup.py --version)Push tag to origingit push --tagsWait for the package to be deployed and then check new version atPyPISteps to release a new version to test PyPIAlmost same as above, but thepush tag stepmust be skipped. Testing version is available attest PyPI.Installing on Windows in developer modeThis is discussed more in detail ininstallDevWin.md.Running the testsYou must run the tests from theimproutils_packagedirectory.
An example command for running the test may be as follows:python tests\test_preprocessing.pyNote: running the test from thetestsdirectory itself will result in errors
Package: improv
improvA flexible software platform for real-time and adaptive neuroscience experiments.improvis a streaming software platform designed to enable adaptive experiments. By analyzing data, such as 2-photon calcium images, as it comes in, we can obtain information about the current brain state in real time and use it to adaptively modify an experiment as data collection is ongoing.This video shows raw 2-photon calcium imaging data in zebrafish, with cells detected in real time byCaImAn, and directional tuning curves (shown as colored neurons) and functional connectivity (lines) estimated online, during a live experiment. Here only a few minutes of data have been acquired, and neurons are colored by their strongest response to visual simuli shown so far.
We also provide up-to-the-moment estimates of the functional connectivity by fitting linear-nonlinear-Poisson models online, as each new piece of data is acquired. Simple visualizations offer real-time insights, allowing for adaptive experiments that change in response to the current state of the brain.How improv worksimprov allows users to flexibly specify and manage adaptive experiments to integrate data collection, preprocessing, visualization, and user-defined analytics. All kinds of behavioral, neural, or modeling data can be incorporated, and input and output data streams are managed independently and asynchronously. With this design, streaming analyses and real-time interventions can be easily integrated into various experimental setups. improv manages the backend engineering of data flow and task execution for all steps in an experimental pipeline in real time, without requiring user oversight. Users need only define their particular processing pipeline with simple text files and are free to define their own streaming analyses via Python classes, allowing for rapid prototyping of adaptive experiments.improv's design is based on a streamlined version of the actor model for concurrent computation. Each component of the system (experimental pipeline) is considered an 'actor' and has a unique role. They interact via message passing, without the need for a central broker. Actors are implemented as user-defined classes that inherit fromimprov'sActorclass, which supplies all queues for message passing and orchestrates process execution and error handling. Messages between actors are composed of keys that correspond to items in a shared, in-memory data store. This both minimizes communication overhead and data copying between processes.InstallationFor installation instructions, please consult thedocson our github.ContactTo get in touch, feel free to reach out on Twitter@annedraelosor@jmxpearson.
Package: improved-backoff
improved_backoffFunction decoration for backoff and retryThis is a fork of an excellent Python librarybackoff. The library was forked on January 26, 2023.
It includes the original version 2.2.1 (October 5, 2022) and a few extra updates.This fork includes 2 PRs proposed in the original repo:Correct check for max_time parameterFix some issues around max_time in _next_waitUsing "timeit" module for time managementThe updated behavior ofmax_timeis that a a function will be retried only if the elapsed time is less,
thanmax_time. Also the remaining time should exceed theinterval(delay between retries).The code that calculates delay time was modified to not generate negative values.
If at the time of calculation elapsed time exceeds specifiedmax_timevalue than delay is set to zero.
If calculated delay time is negative, then it's also set to zero.In order to use this module import it underbackoffalias and use it
the same way as the original moduleimportimproved_backoffasbackoff@backoff.on_exception(backoff.expo,requests.exceptions.RequestException)defget_url(url):returnrequests.get(url)
Package: improvedgan-pytorch
ImproveGan-pytorchImproved Techniques for Training GANs for cifar10 datasetThis is an implementation of Semi-supervised generative adversarial network in the paper Improved Techniques for Training GANs for cifar10 dataset.RelatedOrigin paper:https://arxiv.org/abs/1606.03498Official Implementation(Theano):https://github.com/openai/improved-ganRequirementpython3.5pytorch 1.1.0cuda8.0torchvisionRunpython3 train.pyDefault Set:config.pyResultTest best acc:79.64%.Achieve the accuracy of the original paper.loss curveTrain process of Gen img
Package: improvmxpy
BadgesImprovMX_PY Repository!Welcome to this repository!InstructionPlease install this through pip (Underdevelopment)pipinstallimprovmxpyOr Clone this repo and install it by dogitclonehttps://github.com/dumb-stuff/improvmx_pycdimprovmx_pypythonsetup.pyinstallthis repo is developmentAlso Why I am such an idiot that I forget to add .gitignore D:MemeWhen I forget to add .gitignore to my local repositoryThe size in my PCActual UploadPlease Enjoy my stupidness at this URLhttps://github.com/dumb-stuff/improvmx_py/commit/28913ca8340355ffd75478c42b5e696fa12e328bDOCUMENTATION!!https://improvmxpy.tkDumb factEvery single lines of code in here is get blackked style.UpdateI added brotlipy to requirements and asynchronous deprecated stuff is removed enjoy itHelpPlease go toDocumentation
Package: impsparc
NOTE: This repo is now maintained athttps://github.com/imperva/impsparcImpSpARCAPI Specification Analysis for Risks and ComplianceImpSpARC is a tool that will analyse API Specification for Security Risks and Compliance violations.API Specification captures the design of an API and is an integral part of API development process. The design of an API is captured in various formats such as Swagger, OpenAPI, RAML, etc.ImpSpARC analyses API specifications written in Swagger or OpenAPI format against the security best practices and then provides an assessment score and severity. The ImpSpARC tool also provides a language to codfiy your custom Risk and Compliance voilation rules too.Learn more about the security best practices that is supported out of the box in this tool here -https://www.cloudvector.com/api-security-checklist-secure-api-design/
Package: impt
imPTFast estimator for Lensing Perturbation (impt) on astronomical images using
the auto differentiating function of jax. The code need to be combined with
estimator (e.g.FPFS) compressing image
to catalog. It is a simple code to infer shear from the catalog with correction for noise biasInstallationFor stable (old) version, which have not been updated:pipinstallimptFor developers, please clone the repository:gitclonehttps://github.com/mr-superonion/imPT.git
pipinstall-e.--userSummaryFor the first version ofimPT, we implementFPFSand useimPTto auto
differentiateFPFS.
Package: imptools
imptoolsTools for improving Python imports.Installationpip3installimptoolsDocsimport_path()Import a module from any path on the filesystem.Usually, this would be achieved by adding the parent directory of the module tosys.pathor thePYTHONPATHenvironment variable and then importing it
normally. However, this pollutes Python's import path, which can lead to
accidentally importing the wrong modules. The functionimport_path()avoids
this problem by importing a package from a path on the filesystem without
modifying the Python import path.The module can be either a directory containing__init__.pyor a single file.Relative paths are resolved relative to the directory of the source file that
callsimport_path().importimptoolsmy_module=imptools.import_path('../path/to/my_module',# Path to a module directory or single file.notfound='error',# Raise 'error' or 'ignore' if not found.reload=False,# Whether to import if already available.)importmy_module# Import statement also works.enable_relative()Enable relative imports for scripts that are not executed as module.Usually, scripts that are part of a module and use relative imports must be run
aspython3 -m module.script. However, this requires being in the correct
working directory and can be annoying. Theenable_relative()function allows
to execute those scripts normally aspython3 script.py.Since PEP 366, this can be achieved by setting the__package__variable in
the script and importing the package or making it available on the Pyhton
import path. Theenable_relative()function hides this behind a simple
function that can be imported and called inside the script, before any relative
imports.importimptoolsimptools.enable_relative()# Relative imports...Testspython3 -m pytest tests
Package: impuestito
ImpuestitoDescripcionImpuestito es una API local para calcular impuestos al hacer compras al exterior, tambien tiene una herramienta para saber el precio oficial de diferentes cotizaciones en argentina como el dolar, el euro, dolar blue u otros.InstalandoPuedes instalar o actualizar la API desdepip:$ pip install impuestitoEjemplosCalcular precio de compra al exterior con cualquier monedaNo importa si pones dolares, pesos, euros o cualquier otra moneda ya que la moneda no afecta el calculo# Importamos la funcion de la API
from impuestito import calcularImpuestoPais

# Para mayor comodidad declaramos variable. Esta funcion le pedira a la API que le calcule el impuesto pais a la cantidad que pase de parametro, por ejemplo en este caso calcularemos 230 pesos.
x = calcularImpuestoPais(230)

# Imprimimos el resultado
print(x)

>> {'cantidadVieja': 230, 'agregado': 172.5, 'cantidadFinal': 402.5}

# Si queremos obtener la variable final que seria el total debemos imprimir esto:
print(x["cantidadFinal"])

>> 402.5Obtener precio del dolar oficial# Importamos la funcion de la API
from impuestito import dolarOficial

# Para mayor comodidad declaramos variable. Esta funcion le pedira a la API que le transforme $2 a Pesos
x = dolarOficial.aPesos(2)

# Imprimimos el resultado
print(x)

>> {'usd': 2, 'dolarPrice': 176.0, 'total': 352.0}

# Si queremos obtener la variable final que seria el total debemos imprimir esto:
print(x["total"])

>> 352.0Contacto & DonacionesSi necesitas ayuda con algo o quieres ayudarme donando aqui te dejo algunos datos:Discord: Pina#0001
Web:http://pinayt.tkPaypal:https://paypal.me/pinaytt
Package: impulsare-config
OverviewA config reader, that validates a YAML config file and add default values if required.Extra values won’t be verified, that any component / library defines its own config parameters
in a single configuration file without blocking other to do the same.Seetests/static/for examples.Installation / UsageTo install use pip:$pipinstall--upgradeimpulsare-configExamplefromimpulsare_configimportReader# Main Config Fileconfig_file='config/app.yml'# File with validation rules based on JSON Schemaspecs_file='config/specs.yml'# Default valuesdefault_file='config/default.yml'config=Reader().parse(config_file,specs_file,default_file)Development & Tests$pipinstall-rrequirements.txt$pipinstall-rrequirements-dev.txt$py.test--cov-reporthtml--cov=impulsare_configtests/