Saravanan666/pypi-org huggingface dataset:
Package: carla-scenario-runner
ScenarioRunner for CARLAThis repository contains traffic scenario definition and an execution engine
for CARLA. It also allows the execution of a simulation of the CARLA Challenge.
You can use this system to prepare your agent for the CARLA Challenge.Scenarios can be defined through a Python interface, and with the newest version
the scenario_runner also the upcomingOpenSCENARIOstandard is supported.Getting the ScenarioRunnerUsegit cloneor download the project from this page. Note that the master
branch contains the latest fixes and features, and may be required to use the latest features from CARLA.It is important to also consider the release version that has to match the CARLA version.Version 0.9.11and the 0.9.11 Branch: Compatible withCARLA 0.9.11Version 0.9.10and the 0.9.10 Branch: Compatible withCARLA 0.9.10Version 0.9.9and the 0.9.9 Branch: Compatible withCARLA 0.9.9. Use the 0.9.9 branch, if you use CARLA 0.9.9.4.Version 0.9.8and the 0.9.8 Branch: Compatible withCARLA 0.9.8Version 0.9.7and the 0.9.7 Branch: Compatible withCARLA 0.9.7but not with the later release patch versions.Version 0.9.6and the 0.9.6 Branch: Compatible withCARLA 0.9.6Version 0.9.5andVersion 0.9.5.1: Compatible withCARLA 0.9.5Version 0.9.2: Compatible withCARLA 0.9.2To use a particular version you can either download the corresponding tarball or simply checkout the version tag associated to the release (e.g. git checkout v0.9.5)Currently no build is required, as all code is in Python.Using the ScenarioRunnerPlease take a look at ourGetting starteddocumentation.Challenge EvaluationYou can evaluate your own agents using a reproduction
of the CARLA Challenge by followingthis tutorialContributingPlease take a look at ourContribution guidelines.FAQIf you run into problems, check ourFAQ.LicenseScenarioRunner specific code is distributed under MIT License.
Package: carleslibs
carleslibsCarles Mateo's Python Libraries v. 1.0.8https://blog.carlesmateo.com/carleslibsInstall in Ubuntu with:pip3 install carleslibsIt is shipped with a sample demo application to easily see how it works:carleslibs_demo.pyPackgades available:DateTimeUtils()FileUtils()HashUtils()KeyboardUtils()MenuUtils()OsUtils()PythonUtils()StringUtils()SubProcessUtils()
Package: carlfg
clebgCalculate Clebsch–Gordan coefficientsTypical usage:#!/usr/bin/env python

from cg import ClebschGordan
ClebschGordan.execute('myImage.png',0.5,0.5,'Default','Default')this should create an image with the namemyImage.pngin working directoryFor graphical frontend, open the terminal and type:clebgAdditional modulescgCalculate sum of all natural numbers from 1 to n using Carl Friedrich
Guass[1777-1855, Germany] algorithm.Typical usage:#!/usr/bin/env python

from cg import german
print german.sum(999)gcdTo find GCD/GCF of two numbers by Euclid’s GCD algorithm.Typical usage:#!/usr/bin/env python

from cg import german
print german.gcd(999, 56)InstallationInstall using PyPI:pip install carlfgTo uninstall:pip uninstall carlfgSource code atgithub.com/psachin/carlfg
Package: carlo
carloNamed after the Monte Carlo algorithm, this module displays interactive histograms of streaming/online data. It's meant for quickly visualizing distributions, and it'll keep refining its histogram as new data comes in.Accepts lists, generators, numbers from stdin, or a function to be repeatdly evaluated. Displays data as histograms with automagic bin allocation, and extra statistics in legends.Example 1Compare samples from one 20-sided dice vs three 6-sided dices.Imported:fromcarloimportplot,dplot(lambda:d(20),lambda:d(6)+d(6)+d(6))Or as standalone module (whered(n)simulates the roll of an-sided dice):carlo"d(20)""d(6)+d(6)+d(6)"Example 2Sample values frommax(0.5, random()**0.2).Imported:fromcarloimportplotfromrandomimportrandomplot(lambda:max(0.5,random()**0.2))Or as standalone module (all functions from therandommodule are automatically available).carlo"max(0.5, random()**0.2)"
Package: carmen-cloud-client
Carmen Cloud Client by Adaptive RecognitionPython client forCarmen CloudbyAdaptive Recognition. This unified library provides you with access to both theVehicle APIand theTransportation & Cargo API.Supported API VersionsVehicle API: v1.4Transportation & Cargo API: v1.0🛠️ How to Installpipinstallcarmen-cloud-client🚀 UsageYou can utilize either the Vehicle API or the Transportation & Cargo API based on your needs.🚗 Vehicle APIfromcarmen_cloud_clientimportVehicleAPIClient,SelectedServices,Locationsclient=VehicleAPIClient(api_key="<YOUR_API_KEY>",services=SelectedServices(anpr=True,mmr=True),input_image_location=Locations.Europe.Hungary,cloud_service_region="EU")response=client.send("./car.jpg")print(response)🚚 Transportation & Cargo APIfromcarmen_cloud_clientimportTransportAPIClient,CodeTypeclient=TransportAPIClient(api_key="<YOUR_API_KEY>",code_type=CodeType.ISO,cloud_service_region="EU")response=client.send("./container.jpg")print(response)🔧 DevelopmentFor more information about developing and contributing to this project, seeDEVELOPMENT.md.
Package: carmenproxy
Carmen------.. image:: images/carmen_sandiego.jpg:height: 100px:width: 120 px:scale: 50 %:alt: alternate text:align: right.. image:: https://img.shields.io/pypi/v/carmen.svg:target: https://pypi.python.org/pypi/carmen:alt: Latest PyPI version.. image:: https://travis-ci.org/mark-hoffmann/carmen.png:target: https://travis-ci.org/mark-hoffmann/carmen:alt: Latest Travis CI build status.. image:: https://codecov.io/gh/mark-hoffmann/carmen/branch/master/graph/badge.svg:target: https://codecov.io/gh/mark-hoffmann/carmen:alt: CoverageCarmen is a tool for proxying programmatic HTTP requests. It finds a list of free proxy servers and routes your request traffic randomly, making everyone ask the question "Where in the world [is Carmen SanDiego] are my network requests coming from?"This can be useful for webcrawlers where sites block bots after only a few requests.**Disclaimer: Please use responsibily. Writing a large scale scraper can have detrimental effects on a websites performance and can cost a company a lot of money. DO NOT use this maliciously. Remember, with great power comes great responsibility. **Installation------------Carmen can easily be downloaded straight from github via the following:.. code-block:: pythonpip install carmenproxyUsage-----This package connects to 'https://www.sslproxies.org/' and finds free available proxies to then cycle through, discarding those that do not initially work.To use Carmen, simply instantiate the ProxyRotator object, get the available proxies and pass your url into the *make_request* method. You are then returned a Response object... code-block:: pythonfrom carmenproxy import ProxyRotatorpr = ProxyRotator()pr.get_proxies()r = pr.make_request('http://url.com')r.read()Requirements^^^^^^^^^^^^- bs4- `fake-useragent <https://github.com/hellysmile/fake-useragent>`_Compatibility-------------carmenproxy currently supports Python 3.6Licence-------`MIT <https://github.com/mark-hoffmann/carmenproxy/blob/master/LICENSE.txt>`_Authors-------`carmenproxy` was written by `Mark Hoffmann <markkhoffmann@gmail.com>`_.
Package: carmine
Carmine toolchainAn opinionated Python toolchain made by crushing bugs.UsageAddcarmineas a developer dependency, and all the included tools will be
available for documentation, linting and testing.Runcarmineto see a list of top-level dependencies and version numbers.For documentation on individual tools, see links below.Document toolchainmkdocsmdformat_mkdocsmkdocs-awesome-pages-pluginmkdocs-mermaid2-pluginLinter toolchainblackmdformatreuseTesting toolchaincoveragepytestUnder considerationdutygriffemkdocs-coveragemkdocstringsRecommended extrasCommand line interfaceClickmkdocs-clickSlidesslide-template(MkDocs)Sharing and contributionsCarmine toolchain
https://lofidevops.neocities.org
Copyright 2023 David Seaward and contributors
SPDX-License-Identifier: Apache-2.0Shared under Apache-2.0. We adhere to the Contributor Covenant 2.1, and certify
origin per DCO 1.1 with a signed-off-by line. Contributions under the same
terms are welcome.Submit security and conduct issues as private tickets. Sign commits withgit commit --signoff. For a software bill of materials runreuse spdx. For
more details see CONDUCT, COPYING and CONTRIBUTING.
Package: carnarvon
Carnarvon analyses how old the software system is on a per-line basis andextracts figures and indexes that make it possible to identify how `old' thesoftware is, how much it has been maintained and how much effort it may supposeto maintain it in the future.A lot of software, mostly open source software, is developed using versioncontrol tools from which it is possible to extract even when a single line ofcode was edited for the last time. To collect all this kind of data and analyzeit statistically could show information in terms of software aging andindicators of maintainability could be obtained from different perspectives,systems area and development area, for example.Carnarvon runs on any platform with python 2.3+ interpreter installed. Currentsupported versioning systems are CVS and Subversion.
Package: carnifex
Carnifex provides an api for executing commands.
The processes can be started locally or remotely on another machine with
minimal effort for the user.Carnifex builds on Twisted and uses Twisted Conch for executing commands
on a remote machine.LicenseCarnifex is made available under the MIT license. See the included LICENSE file
for details.ChangesVersion 0.2.5Minimal Py3 portVersion 0.2.4Replace code that used pwd module with cross-platform alternativeVersion 0.2.3Fix pty support for SSHVersion 0.2.2Fix missing packages in distributionLicense and documentation updatesExample scriptsFixed issue with reconnecting after immediate failure in SSH inductorVersion 0.2.1More flexible connection support for ssh inductor (e.g. IPv6)API for connecting Protocol to various SSH sessions (‘shell’, ‘exec’
and ‘subsystem’)Bug fixesVersion 0.2.0Added support for authenticating with stored credentials when using
remote process inductorAdded support for specifying valid known hosts to allow with remote executionAPI change: execute, run, getOutput and getExitCode now take a command object
or a string for the command to execute instead of executable and args.Version 0.1.0Executing and running processes locally and remotely via ssh is supported.Initial alpha release
Package: carnot
carnotcarnotis a module that enables reversible transactions for general purposes. Reversible transactions may make your codes simpler and have better readability inspite of complicated logics.InstallingPython >= 3.8python -m pip install carnotUsagefromcarnotimportreversible_function,transactioncount=0@reverse_functiondefadd(num:int)->None:globalcountcount+=numadd.set_args(num)@add.backwarddef_add(num:int)->None:globalcountcount-=num@transactiondefadd_and_emit_error()->None:add(2)raiseExceptionif__name__=="__main__":try:add_and_emit_error()except:passfinally:print(count)# 0
Package: carol-pdf-generator
carol_pdf_generator is a Python library for creating PDF Files from
images (png, jpg) file. Base64 encoded is also supported.InstallationUse the package managerpipto
install foobar.pipinstallcarol_pdf_generatorUsagefromcarol_pdf_generatorimportget_from_file_list,get_from_base64_listfromurllibimportrequestfile_list=['jpgfile.jpg','pngfile.png']base64_images.append(request.urlopen('https://pastebin.com/raw/k3VZeNHW').read().decode('latin1'))base64_images.append(request.urlopen('https://pastebin.com/raw/CaZJ7n6s').read().decode('latin1'))base64_images.append(request.urlopen('https://pastebin.com/raw/7Asb2iMJ').read().decode('latin1'))from_file=get_from_file_list(file_list)base64=get_from_base64_list(base64_images,'test.pdf')# Returns the PDF base64 encoded data uri and saves the PDF to test.pdfDocument Scan and auto rotationThis library can automatic try to identify paper documents and rotate the image to better fits the PDF Document.base64_jpg=str(DataURI.make(mimetype='image/jpeg',charset='latin1',base64=True,data=request.urlopen('https://raw.githubusercontent.com/danielgatis/docscan/master/examples/doc-1.jpg').read().decode('latin-1')))base64_png_2=str(DataURI.make(mimetype='image/png',charset='latin1',base64=True,data=request.urlopen('https://raw.githubusercontent.com/danielgatis/docscan/master/examples/doc-2.png').read().decode('latin-1')))base64_jpg_3=str(DataURI.make(mimetype='image/jpeg',charset='latin1',base64=True,data=request.urlopen('https://raw.githubusercontent.com/danielgatis/docscan/master/examples/doc-3.jpg').read().decode('latin-1')))base64_images=[base64_jpg,base64_png_2,base64_jpg_3]# Returns the PDF base64 encoded data uri and saves the PDF to test.pdf, correcting the document geometry and auto rotating the imagespdf=get_from_base64_list(base64_images,auto_rotate=True,scan_docs=True,output_file="test.pdf")Merging PDF and imagesThis library you can create a PDF file merging PDF, JPEG and PNG files. Just use merge_pdfs() function using a List() of base64 encoded files as parameter.fromcarol_pdf_generator.carol_pdf_generatorimportmerge_pdfsfiles=[base64_jpg,DataURI.from_file("test.pdf"),base64_jpg1,DataURI.from_file("test2.pdf"),base64_jpg2,DataURI.from_file("test3.pdf")]data=DataURI(merge_pdfs(files))b=BytesIO(data.data)## Some random BytesIO Objectwithopen("test333.pdf","wb")asf:f.write(b.getbuffer())ContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.Please make sure to update tests as appropriate.History0.1.0 (2020-09-15)First release on PyPI.
Package: carom-sblab
# CaromCAROM is the machine learning tool described in the article ‘Metabolic signatures of regulation by phosphorylation and acetylation’ by Chandrasekaran, Lee, Shen and Smith. This tutorial requires basic knowledge of MATLAB and Python, while familiarity with flux balance analysis (FBA) is also helpful. The CAROM software code and the metabolic network models can be downloaded from the Files tab. Installation## InstallationWe can install carom with` python setup.py install `
Package: carooline-tecrmi-seamless
TecRMI Seamless login APPTecRMI Seamless catalog login URL generationInstallationpip install carooline-tecrmi-seamlessConfigurationCreate a.tecrmi.envfile in your~/or Home folder with global TecRMI credentials:TECRMI_WEBCAT_URL = "https://web.tecalliance.net"
TECRMI_WEBCAT_WSDL = 'https://webservice.tecalliance.services/webcat30/v1/services/WebCat30WS.soapEndpoint?wsdl'
TECRMI_CATALOG = "rmi-iazduih-2"
TECRMI_USERNAME = "username"
TECRMI_PASSWORD = "password"
TECRMI_DEFAULT_TTL = 31536000
TECRMI_LANG = "fr"This is not mandatory, you can also pass each arguments directly to the command line.UsageCommand should be installedwich carooline-tecrmi-seamlessShow version:carooline-tecrmi-seamless --versionHelpTo see the different options and commands available:carooline-tecrmi-seamless --helpShould outputUsage:carooline-tecrmi-seamless[OPTIONS]COMMAND[ARGS]...

Options:-v,--versionShowtheapplication'sversionandexit.--install-completion[bash|zsh|fish|powershell|pwsh]Installcompletionforthespecifiedshell.--show-completion[bash|zsh|fish|powershell|pwsh]Showcompletionforthespecifiedshell,tocopyitorcustomizetheinstallation.--helpShowthismessageandexit.

Commands:get-api-keyGetApiKeyget-linkGetAPIKeyandgenerateTecRMIseamlessdeeplinkfor...Generate API Keycarooline-tecrmi-seamless get-api-keyThis will output your APIKey in the shell.Get Seamless linkBasic command to get a linkcarooline-tecrmi-seamless get-linkGet RMI webapp link directly on vehicle with KType:Giving acarid (KType):carooline-tecrmi-seamless get-link --type-id=9465Giving atruckid (NType):carooline-tecrmi-seamless get-link --vehicle-type=trucks --type-id=13290OptionsOptions:--catalogTEXT[envvar:TECRMI_CATALOG;default:TecrmiCatalog]--usernameTEXT[envvar:TECRMI_USERNAME;default:TecrmiUsername]--passwordTEXT[envvar:TECRMI_PASSWORD;default:TecrmiPassword]--default-ttlINTEGERLivingdurationofthegeneratedTecRMIAPIKey[envvar:TECRMI_DEFAULT_TTL;default:31536000]--wsdlTEXT[envvar:TECRMI_WEBCAT_WSDL;default:https://webservice.tecalliance.services/webcat30/v1/services/WebCat30WS.soapEndpoint?wsdl]--langTEXT[envvar:TECRMI_LANG;default:fr]--urlTEXT[envvar:TECRMI_WEBCAT_URL;default:https://web.tecalliance.net]--module[rmi|parts|home][default:TecRMIModule.rmi]--vehicle-type[cars|trucks][default:VehicleType.cars]--type-idTEXTKTypeorNType(Withoutthe100prefix)--display[modules|components][default:Display.modules]--sivTEXTSIVsearchissubjecttotheaccessrightsofthecredentials--vinTEXTVINsearchissubjecttotheaccessrightsofthecredentials--helpShowthismessageandexit.
Package: carotte
Carotte is a very lightweight Celery on zmq.Installpip install carotteGetting startedCreate yourapp.py:from carotte import Carotte

my_app = Carotte()

@my_app.task
def hello_world(name):
    return 'Hello %s!' % nameRun your worker (default on “tcp://127.0.0.1:5550”):carotte worker --app app:my_appRun tasks:>>> from app import hello_world
>>> t = hello_world.delay(['Carotte'])
>>> t.success
>>> True
>>> t.result
>>> 'Hello Carotte!'Or run a client (if don’t have tasks on your system):>>> from carotte import Client
>>> client = Client()
>>> task = client.run_task('hello_world', ['Carotte'])
>>> task.success
>>> True
>>> task.result
>>> 'Hello Carotte!'Scheduled tasksCarotte is not a scheduler, its an asynchronous tasks runner.
But you can really set up scheduled tasks withschedule.Yourapp.py:import requests
from carotte import Carotte

my_app = Carotte()

@app.task
def get(url):
    r = requests.get(url)
    if r.status_code != 200:
        # Do stuff
        return False
    return TrueYourscheduler.py:import time
import schedule
from app import get

schedule.every(10).seconds.do(get, 'http://google.com')

while True:
    schedule.run_pending()
    time.sleep(1)Run your worker and your scheduler:carotte worker --app app:my_app
python scheduler.py
Package: carpedm
Docs|Install Guide|TutorialDescriptionCarpeDMis a general library for downloading, viewing, and manipulating image data.
Originally developed as a ChARacter shaPE Data Manager, CarpeDM aims to make Japanese character shape (字形) data
and other image datasets more accessible to machine learning researchers.Datasets Currently Available for DownloadIDDatasetpmjtcPre-Modern Japanese Text Character Shapes Dataset (日本古典籍字形データセット),provided by the Center for Open Data in the Humanities (CODH).Though still in the early stages of development, a high-level interface is also provided forAutomatic model-ready data generation.Flexible training of models with a variety of deep learning frameworks.Currently supported deep learning frameworks:TensorFlow
Package: carpet-concentrations
Carpet - ConcentrationsCore tools for the development of greenhouse gas concentration input files (i.e. flying carpets).Repository health :PyPI :Other info :Full documentation can be found at:carpet-concentrations.readthedocs.io. We recommend reading the docs there because the internal documentation links don't render correctly on GitHub's viewer.InstallationCarpet - Concentrations can be installed with conda or pip:pipinstallcarpet-concentrations
condainstall-cconda-forgecarpet-concentrationsAdditional dependencies can be installed using# To add plotting dependenciespipinstallcarpet-concentrations[plots]# To add notebook dependenciespipinstallcarpet-concentrations[notebooks]# If you are installing with conda, we recommend# installing the extras by hand because there is no stable# solution yet (issue here: https://github.com/conda/conda/issues/7502)For developersFor development, we rely onpoetryfor all our
dependency management. To get started, you will need to make sure that poetry
is installed
(instructions here,
we found that pipx and pip worked better to install on a Mac).For all of work, we use ourMakefile.
You can read the instructions out and run the commands by hand if you wish,
but we generally discourage this because it can be error prone.
In order to create your environment, runmake virtual-environment.If there are any issues, the messages from theMakefileshould guide you
through. If not, please raise an issue in theissue tracker.For the rest of our developer docs, please see.
Package: carpo
CarpoAn irregular satellite of Jupyter.A command line utility to run, profile, and save Jupyter notebooks.InstallationCarpo may be installedfrom pypi:pip install carpoTested against python 2.7, 3.4, 3.5, 3.6.QuickstartUsecarpo run path/to_notebook.ipynbto run a notebook.carpo show path/to_notebook.ipynbwill show data from past runs.More depthCarpo plays nicely with shell expansion:Carpo has gives helpful messages when successfully running notebooks:Carpo shows the cell and the error when a run fails:Also, carpo plays nicely with git, and will remember if a notebook has been run:HelpIncludes help messages:$ carpo --help
Usage: carpo [OPTIONS] COMMAND [ARGS]...

  Base for cli commands.

Options:
  --help  Show this message and exit.

Commands:
  run   Try to re-run all notebooks.
  show  View status of notebooks
Package: carport
CarportDependency Graph fromPython imports (CARPORT).InstallationpipinstallcarportUsage# WIPfromcarport.coreimportImportGraphdepg=ImportGraph(root='..',project='carport')# vars(depg)ignore_nodes=['io','IPython','pprint','subprocess']dot=depg.to_dot(ignore_nodes=ignore_nodes)dot'digraph  {\n"carport.vis";\n"carport.core";\npathlib;\nnetworkx;\nast;\ngraphviz;\nkroki;\n"carport.vis" -> "carport.core"  [key=0];\npathlib -> "carport.core"  [key=0];\nnetworkx -> "carport.core"  [key=0];\nast -> "carport.core"  [key=0];\ngraphviz -> "carport.vis"  [key=0];\nkroki -> "carport.vis"  [key=0];\n}\n'dotg=depg.draw_dot(ignore_nodes=ignore_nodes)dotgd2=depg.to_d2(ignore_nodes=ignore_nodes)d2'\n        vars: { \n            d2-config: { \n                layout-engine: elk \n                } \n            }\n        direction: right\n        **.style.border-radius: 99\n        *.style.font: mono\n        \nsubprocess -> carport.vis\nIPython -> carport.vis\nast -> carport.core\npathlib -> carport.core\nnetworkx -> carport.core\nio -> carport.core\ngraphviz -> carport.vis\ncarport.vis -> carport.core\nkroki -> carport.vis\nio: null\nIPython: null\npprint: null\nsubprocess: null'd2g=depg.draw_d2(ignore_nodes=ignore_nodes)d2gTODOs:[] imports concerning__init__.py[]py_d2to d2 (likegraphvizto graphviz)Relatedimport-deps:Githubpydeps:Githubsnakefood3:Githubkroki:online editor,python-interfaceD2:doc,online
editor,python-interfaceStatisticsCopyrightCopyright © 2024 onward Felidz. All rights reserved, with certain parts
of the project specifically licensed and subject to different terms and
conditions, e.g., source code of the project under the Apache-2.0
license; you may not use this project except in compliance with the
license.CitationBibTeX citation:@misc{felidz2024,author={Felidz},title={Carport},year={2024},publisher={GitHub},journal={GitHub repository},howpublished={\url{https://github.com/felidz/carport}}}
Package: carpo-student
Installation instructions for studentsStudents must have installed Anaconda already.Please follow these steps:Open Anaconda.Open the Jupyter Lab app.Navigate to the course directory or create a new course directory.Download the installation script.For MacOS, downloadinstall_carpo_osx.pyFor Windows, downloadinstall_carpo_win.pyUpload the script to the course directory in Jupyter Lab.Click on "+" and open a new Terminal.Execute this command in Terminal.For MacOS:python install_carpo_osx.pyFor Windows:python install_carpo_win.pyClose Jupyter Lab and Anaconda.Open the folder that has the script and double click onRUN_JUPYTER_LAB.command.Configuration of the extensionYour instructor will walk you through the configuration process.