Saravanan666/pypi-org huggingface dataset:
Package: ott
You might be looking for help, but this is all I can do:usage: xo [-h] path

exofrills: your text has been edited...but you are still hungry.

positional arguments:
  path        path to file, may include colon separated line and col numbers,
              eg 'path/to/xo.py:10:42'

optional arguments:
  -h, --help  show this help message and exitSomeone else made avideo tutorialand posted
it on YouTube within an hour of the 0.1 release.get xoInstall from the cheeese shop withpiporeasy_install:$pipinstallexofrills$easy_installexofrillsFork xo fromgithub:$gitclonehttps://github.com/scopatz/xo.gitkey commandsesc:get helpctrl + o:save file (write-out)ctrl + x:exit (does not save)meta + s:select pygments stylectrl + f:insert file at current positionctrl + y:go to line & column (yalla, let’s bounce)ctrl + k:cuts the current line to the clipboardctrl + u:pastes the clipboard to the current linectrl + t:clears the clipboard (these spell K-U-T)ctrl + w:set regular expression and jump to first matchmeta + w:jump to next match of current regular expressionctrl + r:set substitution for regular expression and replace first matchmeta + r:replace next match of current regular expression
Package: otta
OttaOtta is acompose file inheretancehelperRationaleFor simple usecases builtin defaults of docker-compose are just fine.
But systems do grow and the following problems are appearing:shell commands are becoming longercode duplication in compose files is getting worseproject root directory is getting bloated with compose filescomplex compose configurations need up to date documentationExampleWithoutOtta:docker-compose-fbase.yml-flocal.yml-fmock_emails.yml-plocalupWithOtta:ottaupOtta file and otta directoryOtta directory:otta
├── base.yml
├── local.yml
├── mock_mail_server.yml
├── prod_common.yml
├── prod_a.yml
├── prod_b.yml
└── otta.ymlOtta file:files:-base.yml-local.yml-mock_mail_server.yml-prod_common.yml-prod_a.yml-prod_b.ymldefault_recipe:localrecipes:local:project_name:localfiles:-base.yml-local.ymloptions:mockmail:files:-mock_mail_server.ymlprod_a:files:-base.yml-prod_common.yml-prod_a.ymlprod_b:project_name:productionfiles:-base.yml-prod_common.yml-prod_b.ymlExplanationOtta configuration is a directory with compose files and single otta file.Otta files definesroot propertyfilesthat defines all the files that can be used in recipesroot propertydefault_recipethat defines... well, default recipethe most important property,recipesdefines available recipesEach recipe hasname, 1 to 40 symbols: letters, numbers and underscoresoptionalproject_name, with identical restrictionsfiles, list of files defined in root propertyoptions, optional definition of available optionsOptions have name (1 to 20 symbols, numbers letters and underscores) andfilesproperty.CLI referenceOtta and it's variants determine recipe fromOTTA_RECenv variable.Recipe specification is either just recipe (local), or recipe with options (local+mockmail)OTTA_FILEis path to otta file, absolute or relativeSettingOTTA_DEBUGto1makes the program print debug infoottabinaryottacall is equal todocker-compose -f base.yml -f local.yml -p localskarabinaryskaracall is equal todocker stack deploy -c base.yml -c local.ymlproject_nameis ignoredkumlabinarykumlacall is equal tokompose -f base.yml -f local.ymlproject_nameis ignoredContributingPlease report all problems to GitLabPull requests are welcome
Package: otter-grader
Otter-GraderOtter Grader is a light-weight, modular open-source autograder developed by the Data Science
Education Program at UC Berkeley. It is designed to work with classes at any scale by abstracting
away the autograding internals in a way that is compatible with any instructor's assignment
distribution and collection pipeline. Otter supports local grading through parallel Docker
containers, grading using the autograder platforms of 3rd party learning management systems (LMSs),
the deployment of an Otter-managed grading virtual machine, and a client package that allows
students to run public checks on their own machines. Otter is designed to grade Python scripts and
Jupyter Notebooks, and is compatible with a few different LMSs, including Canvas and Gradescope.DocumentationThe documentation for Otter can be foundhere.ContributingSeeCONTRIBUTING.md.ChangelogSeeCHANGELOG.md.
Package: otter-grader-labextension
otter_grader_labextensionA JupyterLab extension for Otter-Grader.RequirementsJupyterLab >= 4.0.0InstallTo install the extension, execute:pipinstallotter_grader_labextensionUninstallTo remove the extension, execute:pipuninstallotter_grader_labextensionContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the otter_grader_labextension directory# Install package in development modepipinstall-e"."# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstallpipuninstallotter_grader_labextensionIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedotter-grader-labextensionwithin that folder.Testing the extensionFrontend testsThis extension is usingJestfor JavaScript code testing.To execute them, execute:jlpm
jlpmtestIntegration testsThis extension usesPlaywrightfor the integration tests (aka user level tests).
More precisely, the JupyterLab helperGalatais used to handle testing the extension in JupyterLab.More information are provided within theui-testsREADME.Packaging the extensionSeeRELEASE
Package: otterpy
OtterOtter is framework for creating microservices in Flask like fassion usingRPCcommunication via message queue. Its built in top ofkiwiPylibrary.ApplicationSimple Otter application looks liks this.fromotterimportOtterimporthashlibimporttimeapp=Otter()@app.add_broadcast(["message.*"])defexample_broadcast(comm,body,sender,subject,correlation_id):print(f"Received{body}")@app.add_task("example.task")defexample_task(comm,delay):time.sleep(delay)print(f"Done waiting{delay}seconds")@app.add_rpc("example.hash")defexample_hash(comm,data):return{"result":hashlib.sha256(data.encode("utf-8")).hexdigest()}app.run("amqp://guest:guest@127.0.0.1/",debug=True)ClientYou can communicate with Otter application usingClient.fromotterimportClientclient=Client("amqp://guest:guest@127.0.0.1/")ifclient.comm:# Broadcast message to anyone who listensclient.broadcast("otter",subject="message.send")# Send task to remote workerclient.task(5)# Send rpc request to hash "otter" and wait for the resultdata=client.rpc("example.hash","otter").result()client.close()BlueprintsYou can use Flask like blueprints in order to organize and split your application into different modules.fromotterimportBlueprintblueprint=Blueprint()@blueprint.add_rpc("hello.world")defexample_hash(comm,data):return{"result":"Hello world from blueprint!"}After that blueprint can be initialized usingregister_blueprintmethod.app.register_blueprint(blueprint)DecoratorsIn most cases you will use JSON to communicate between your microservices, thats why Otter has built in decorator@use_argswhich uses Marshmallow for data validation.frommarshmallowimportfields,validatefromotter.decoratorsimportuse_argslogin_args={"password":fields.Str(required=True,validate=validate.Length(min=8)),"email":fields.Email(required=True)}@app.add_rpc("auth.login")@use_args(login_args)deflogin(comm,data):return{"args":data}ForewordThis is experimental software, any contributions are welcome!
Package: otter-report
Otter is a simple HTML report generator for Python jobs. Otter was
designed to produce reports on long-running jobs on remote machines,
and send them to a web server, but it’s able to process many different
outputs from Python scripts, and convert them into neat and readible HTML pages.Otter makes use of Twitter Bootstrap to make an easily themed layout for its output.Free software: ISC licenseDocumentation:https://code.daniel-williams.co.uk/otter/Quick ExampleOtter can be used to produce reports containing a mixture of prose, data, and figures with minimal code.This report was generated with just a small number of lines of Python:import otter
import otter.bootstrap as bt

report = otter.Otter("index.html", author="Daniel Williams", title="Test Page", author_email= "daniel.williams@ligo.org")


with report:
    report + "#Section Title"

    report + "Lorem ipsum dolor sit amet..."


with report:

    row = bt.Row(3)

    import numpy as np
    import matplotlib.pyplot as plt

with report:
    f, ax = plt.subplots(1,1)
    x = np.linspace(0,10, 100)
    ax.plot(x, np.sin(x))

    row[1] + f

    row[0] + {"mass": "1kg", "price": "$1000", "area": 400}

    report + row

with report:
    report + "##Subsection Header"
    report + "Fusce vel lectus ultricies,... "FeaturesTODO Add support for pandas data tablesTODO Add support for custom headers and footersCreditsTools used in rendering this package:Cookiecuttercookiecutter-pypackageHistory0.0.1 (2016-01-30)First release on PyPI.0.2.0 (2018-07-18)“Beinn Dorain”New API for adding material to reportsFull support for matplotlib plotsSupport for multiple data types in reports0.3.0 (2020-03-02)“Beinn an Dothaidh”Improved Python 3 support.Removed Python 2 support.
Package: otters
# otters.pyOtters are smaller [pandas](https://github.com/pandas-dev/pandas) with less
features.## AuthorJacopo Notarstefano [(@Jaconotar)](https://twitter.com/Jaconotar)## LicenseMIT# Changes## Version 0.0.1 (released 2017-01-12)### What’s newInitial public release.
Package: otter-service-stdalone
otter-service-standaloneUse:InstructionsDeploymentThe cloud deployment is configured using a helm chart. The branch you are on(e.g. dev, staging, prod) determines what environment/k8 namespace you deploy into.See the ./deploy.sh for details. The script will determine the branch and
deploy properly. If you are on the dev branch and pass "build" as an argument,
(e.g../deploy.sh build) then the docker image used on the container is built and pushed to the gcloud image repository (gcr.io). You need to be sure to bump
the version number in src/otter_service_stdalone/init.py before a new build.VersionA git tag with the version is pushed to git whenever the version is bumped and
deployed in production.git tagwill show you the format of the tag(e.g. 0.0.30)
Package: otter-submit
Otter SubmitThis JupyterLab extension places a "Submit" button in the Jupyter Notebook header. The submit button is used to submit a notebook to otter-service for grading.DevelopmentCreate your environment however you like:mamba create -n otter-submit -c conda-forge python==3.11mamba activate otter-submitMake changes and run these commands:python3 -m pip install -ve .jlpm run buildjupyter lab
Package: otterworks-circuitpython-drv8305
IntroductionSPI driver for the Texas Instruments DRV8305 Three-Phase Gate Driver.Patterned after the Adafruit BME280 CircuitPython driver, since that’s what I had lying around to hack, test, and compare against.Installation and DependenciesThis driver depends on:Adafruit CircuitPythonBus DevicePlease ensure that the driver and all dependencies are available on the
CircuitPython filesystem.  This can be most easily achieved by downloading and
installing the latestAdafruit library and driver bundleon your device.Installing from PyPIOn supported GNU/Linux systems like the BeagleBone Black, you can install the driver locallyfrom
PyPI. To install for current user:pip3install--userotterworks-circuitpython-drv8305To install system-wide (this may be required in some cases):sudopip3installotterworks-circuitpython-drv8305To install in a virtual environment in your current project:mkdirproject-name&&cdproject-namepython3-mvenv.envsource.env/bin/activatepip3installotterworks-circuitpython-drv8305Usage Exampleimporttimeimportboardimportbusioimportdigitalioimportotterworks_drv8305spi=busio.SPI(board.SCK_1,board.MISO_1,board.MOSI_1)cs=digitalio.DigitalInOut(board.P9_17))drv8305=otterworks_drv8305.OtterWorks_DRV8305(spi,cs)whileTrue:print(drv8305._get_warning_watchdog_reset())time.sleep(1)ContributingContributions are welcome! Please read ourCode of Conductbefore contributing to help this project stay welcoming.DocumentationTODO: build and share on readthedocs.org
Package: ottoai
Otto: The minimalistic Framework to build AI AssistantsWelcome to Otto, the most minimalistic library for building intelligent AI Assistants, adept for also handling multitenancy and secure authentication.pip install ottoaiExampleTurn the Github SDK into an agent that can answer questions from Github live data using pandas to calculate results.importottoaiimportopenaiimportpandasfromgithubimportGithub,Auth# create an assistanteve=ottoai.Assistant(name="eve",personality="Like Scarlett Johansson with John Oliver's wits",llm_engine=openai,model="gpt-4")# Make your agent capable of answering questions and do all things Github, by simply passing the sdk module# Remember. Just pass objects as skills, and Otto will figure out the rest.eve.add_skill(Github)eve.add_skill(pandas)# Start a conversation and add user specific context (variable names must be interpretable, Otto will take care of the rest)# In this case the assistant will need access token to github, this is so you can pass context dynamically (solving for multitenancy)conversation=eve.start_conversation(user_name="Bob",user_context_variables={"github_api_token":"sometoken"})# ask a questionresponse=conversation.message("Who were the last 5 people to star the mindsdb/otto repo?")As simple as that, no need to build tools or chaining, just pass objects as skills, otto figures out the rest.
Package: ottoanime
Make sure you have upgraded version of pipWindowspy -m pip install --upgrade pipLinux/MAC OSpython3 -m pip install --upgrade pipCreate a project with the following structurepackaging_tutorial/
├── LICENSE
├── pyproject.toml
├── README.md
├── setup.cfg
├── src/
│   └── example_package/
│       ├── __init__.py
│       └── example.py
└── tests/
touch LICENSE
touch pyproject.toml
touch setup.cfg
mkdir src/mypackage
touch src/mypackage/__init__.py
touch src/mypackage/main.py
mkdir testspyproject.tomlThis file tells tools like pip and build how to create your project[build-system]
requires = [
    "setuptools>=42",
    "wheel"
]
build-backend = "setuptools.build_meta"build-system.requires gives a list of packages that are needed to build your package. Listing something here will only make it available during the build, not after it is installed.build-system.build-backend is the name of Python object that will be used to perform the build. If you were to use a different build system, such as flit or poetry, those would go here, and the configuration details would be completely different than the setuptools configuration described below.Setup.cfg setupUsing setup.cfg is a best practice, but you could have a dynamic setup file using setup.py[metadata]
name = example-pkg-YOUR-USERNAME-HERE
version = 0.0.1
author = Example Author
author_email = author@example.com
description = A small example package
long_description = file: README.md
long_description_content_type = text/markdown
url = https://github.com/pypa/sampleproject
project_urls =
    Bug Tracker = https://github.com/pypa/sampleproject/issues
classifiers =
    Programming Language :: Python :: 3
    License :: OSI Approved :: MIT License
    Operating System :: OS Independent

[options]
package_dir =
    = src
packages = find:
python_requires = >=3.6

[options.packages.find]
where = srcRunning the buildMake sure your build tool is up to dateWindowspy -m pip install --upgrade buildLinux/MAC OSpython3 -m pip install --upgrade buildCreate the buildpy -m buildReferenceshttps://packaging.python.org/tutorials/packaging-projects/
Package: ottobox
ottoboxA Custom Jupyter Widget LibraryInstallationYou can install usingpip:pipinstallottoboxIf you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
the nbextension:jupyternbextensionenable--py[--sys-prefix|--user|--system]ottoboxDevelopment InstallationCreate a dev environment:condacreate-nottobox-dev-cconda-forgenodejsyarnpythonjupyterlab
condaactivateottobox-devInstall the python. This will also build the TS package.pipinstall-e".[test, examples]"When developing your extensions, you need to manually enable your extensions with the
notebook / lab frontend. For lab, this is done by the command:jupyter labextension develop --overwrite .
yarn run buildFor classic notebook, you need to run:jupyter nbextension install --sys-prefix --symlink --overwrite --py ottobox
jupyter nbextension enable --sys-prefix --py ottoboxNote that the--symlinkflag doesn't work on Windows, so you will here have to run
theinstallcommand every time that you rebuild your extension. For certain installations
you might also need another flag instead of--sys-prefix, but we won't cover the meaning
of those flags here.How to see your changesTypescript:If you use JupyterLab to develop then you can watch the source directory and run JupyterLab at the same time in different
terminals to watch for changes in the extension's source and automatically rebuild the widget.# Watch the source directory in one terminal, automatically rebuilding when neededyarnrunwatch# Run JupyterLab in another terminaljupyterlabAfter a change wait for the build to finish and then refresh your browser and the changes should take effect.Python:If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.
Package: otto-ml
OTTOOtto is a simple Boilerplate for Machine Learning projects integrated with MLflow that creates a basic directory structure to organize your code and data.
Otto is strongly based onCookiecutter, if you need something more complete is a good desition to visit they repo.├── MLproject
├── README.md
├── Dockerfile
├── build_image.sh
├── .gitignore
├── data
│   ├── processed/
│   └── raw/
├── notebooks/
├── src/
│   ├── data/
│   │   └── __init__.py
│   │   └── data_preparation.py
│   ├── features/
│   │   └── __init__.py
│   ├── models/
│   │   └── __init__.py
│   │   └── model.py
│   ├── train.py
│   ├── settings.py
│   └── __init__.py
└── tests/UsageSimple install otto using pip as followspip install otto-mland use ottootto --name new-projector simple use it with out params and let otto guides you 😉ottoand that's it, Now you can jump to code your model!Ok, but... what this solve?That is a pretty good question. The first attempt is to simplify the startup of a new machine learning project generating most, not machine-learning related code. Like the configuration of the docker image viaDockerfileor theMLProjectsetup and the connection with the Mlflow tracking server if you have set up one using ENV variables.But to make it cristal water, let show how it will be a standard use of theottopackage.The Titanic Competition Example... In development ...
Package: ottopy
ottopy[![Build Status](https://travis-ci.org/Varsito Pvt Ltd/ottopy.svg?branch=master)](https://travis-ci.org/VarsitoPvt Ltd/ottopy)
[![codecov](https://codecov.io/gh/Varsito Pvt Ltd/ottopy/branch/master/graph/badge.svg)](https://codecov.io/gh/VarsitoPvt Ltd/ottopy)A configurable maze libraryInstallationYou can install usingpip:pipinstallottopyIf you are using Jupyter Notebook 5.2 or earlier, you may also need to enable
the nbextension:jupyternbextensionenable--py[--sys-prefix|--user|--system]ottopyDevelopment InstallationCreate a dev environment:condacreate-nottopy-dev-cconda-forgenodejsyarnpythonjupyterlab
condaactivateottopy-devInstall the python. This will also build the TS package.pipinstall-e".[test, examples]"When developing your extensions, you need to manually enable your extensions with the
notebook / lab frontend. For lab, this is done by the command:jupyter labextension develop --overwrite .
yarn run buildFor classic notebook, you need to run:jupyter nbextension install --sys-prefix --symlink --overwrite --py ottopy
jupyter nbextension enable --sys-prefix --py ottopyNote that the--symlinkflag doesn't work on Windows, so you will here have to run
theinstallcommand every time that you rebuild your extension. For certain installations
you might also need another flag instead of--sys-prefix, but we won't cover the meaning
of those flags here.How to see your changesTypescript:If you use JupyterLab to develop then you can watch the source directory and run JupyterLab at the same time in different
terminals to watch for changes in the extension's source and automatically rebuild the widget.# Watch the source directory in one terminal, automatically rebuilding when neededyarnrunwatch# Run JupyterLab in another terminaljupyterlabAfter a change wait for the build to finish and then refresh your browser and the changes should take effect.Python:If you make a change to the python code then you will need to restart the notebook kernel to have it take effect.
Package: ottr
pyOTTRManipulateOTTR Reasonable Ontology Templatesin Python.Package documentationOTTR documentationSupported features:Definition and execution of templatesin thestOTTR syntaxNesting templatesType checkingNon blank,Optionalanddefault valuesfor template parameters.RDF and RDFS templatesfrom theOTTR template libraryare loaded by default.In development:Expansion modesSupport forOWL templatesfrom the template libraryInstallationUsing pip (recommended)pip install ottrManual installationgit clone
cd pyOTTR/
python setup.py installGetting startedThe main class to manipulate isOttrGenerator, which is used to load
OTTR templates and expand template instances. So, in practice, you only
need to create a new generator, load some templates and then execute
your instances to produce RDF triples.By default,all templatesfrom theOTTR template
libraryare loaded when the generator is
created.fromottrimportOttrGenerator# An OttrGenerator is used to load templates and expand instancesgenerator=OttrGenerator()# Load a simple OTTR template definitiongenerator.load_templates("""
  @prefix ex: <http://example.org#>.

  ex:FirstName [ottr:IRI ?uri, ?firstName] :: {
    ottr:Triple (?uri, foaf:firstName, ?firstName )
  } .

  ex:Person[ ?firstName ] :: {
    ottr:Triple (_:person, rdf:type, foaf:Person ),
    ex:FirstName (_:person, ?firstName)
  } .
""")# Parse and prepare an instance for executioninstances=generator.instanciate("""
  @prefix ex: <http://example.org#>.

  ex:Person("Ann").
""")# Execute the instance, which yield RDF triples# The following prints (_:person0, rdf:type, foaf:Person) and (_:person0, foaf:firstName, "Ann")fors,p,oininstances.execute(as_nt=True):print("# ----- RDF triple ----- #")print((s,p,o)