Saravanan666/pypi-org huggingface dataset:
Package: dobject
.. raw:: html<!-- starttoc -->Table of contents=================-  `Dobject <#dobject>`__-  `Install <#install>`__-  `Usage <#usage>`__-  `Access all the dictionary keys like objectattributes <#access-all-the-dictionary-keys-like-object-attributes>`__.. raw:: html<!-- endtoc -->Dobject=======Easily convert dicts to objects. Access key values as object attributes.Install-------.. code:: shellpip install dobjectUsage-----.. code:: pythonfrom dobject import DObjectimport datetimedict_data = {'data': [{'effective_date': datetime.datetime(2017, 3, 2, 0, 0),'new_symbol': 'MOMENT','old_symbol': '2E'},{'effective_date': datetime.datetime(2017, 4, 27, 0, 0),'new_symbol': 'AAAG','old_symbol': 'AAAGE'},{'effective_date': datetime.datetime(2016, 11, 30, 0, 0),'new_symbol': 'PIOI','old_symbol': 'ACPW'}],'ticker_changes': [{'new_symbol': 'MOMENT', 'old_symbol': '2E'},{'new_symbol': 'AAAG', 'old_symbol': 'AAAGE'},{'new_symbol': 'PIOI', 'old_symbol': 'ACPW'}]}obj = DObject(dict_data)# Access all the dictionary keys like object attributesprint obj.ticker_changes[0].old_symbolYou could also access the values like you would with a dictionary... code:: pythonprint obj['ticker_changes'][0]['old_symbol']
Package: dobles
Fork of https://www.github.com/uber/doubles with support for python 3.8+doblesis a Python package that provides test doubles for use in automated tests.It provides functionality for stubbing, mocking, and verification of test doubles against the real objects they double.
In contrast to the Mock package, it provides a clear, expressive syntax and better safety guarantees to prevent API
drift and to improve confidence in tests using dobles. It comes with drop-in support for test suites run by Pytest or standard unittest.DocumentationDocumentation is available athttp://dobles.readthedocs.org/en/latest/.DevelopmentSource code is available athttps://github.com/smartfastlabs/dobles.To install the dependencies on a fresh clone of the repository, runmake bootstrap.To run the test suite, runmake test.To build the documentation locally, runmake docs.LicenseMIT:http://opensource.org/licenses/MIT
Package: dobotapi
Dobot APIPowerful python api for handling dobot magician and conveyor beltReport Bug·Documentation·Request FeatureTable of ContentsAbout The ProjectBuilt WithGetting StartedPrerequisitesInstallationLicenseContactAbout The ProjectLibrary forDobot Magicanusing python because the provided one is piece of crap.(back to top)Built Withpydobotpydobot2(back to top)Getting StartedSeehttps://stax124.github.io/DobotAPI(back to top)LicenseSeeLICENSEfor more information.(back to top)ContactTomáš Novák -tamoncz@gmail.comBenda Filip -benda.filip.fb@gmail.comProject Link:https://github.com/Stax124/DobotAPI(back to top)
Package: dobot-util
Ergonomic and type-hinted Python API for Dobot ProductsA work-in-progress API, but with the goal of becoming the standard communication layer with Dobot Product's TCP-IP Protocol.The majority of the Dobot TCP-IP methods are renamed to adhere to snakecase formatting and increase readability.Unique to this API, each method could potentially return an error, this allows for you to determine how errors should be handled in your implementation.How to use this API?To use the API, just simply download the PyPi package throughpip install dobot-util.After installing the package, you can import theDobotclass and theDobotErrortype.Its important to note that you need to switch your Dobot Robot into TCP-IP Development Mode in Remote Control settings otherwise it won't connect.Once this is all configured, you can provide the IP of the robot on your local network and easily start a connection and issue commands from each component.There are three components - Dashboard, Movement, and Feedback. Each of them have their own responsibility and their name explains their role well-enough.For actual examples of the API being used in code, check the examples directory.
Package: dobson
DOBSONDobson file parserCode:https://github.com/gosow9/dobsonDocs:https://dobson.readthedocs.io/en/master/PyPI:https://pypi.org/project/dobson/CI:https://github.com/gosow9/dobson/actions/Featuresdobsonpython packageTODO Document aGreat FeatureTODO Document anotherNice FeatureTested against multipleplatformsandpythonversionsDevelopmentHere are some useful notes related to doing development on this project.Test Suite, usingpytest, located intestsdirParallel Executionof Unit Tests, on multiple cpu’sDocumentation Pages, hosted onreadthedocsserver, located indocsdirAutomation, usingtox, driven by singletox.inifileCode CoveragemeasuringBuild Command, using thebuildpython packagePypi Deploy Command, supporting upload to bothpypi.organdtest.pypi.orgserversType Check Command, usingmypyLintCheckandApplycommands, usingisortandblackCI Pipeline, running onGithub Actions, defined in.github/Job Matrix, spanning differentplatform’s andpython version’sPlatforms:ubuntu-latest,macos-latestPython Interpreters:3.6,3.7,3.8,3.9,3.10Parallel Jobexecution, generated from thematrix, that runs theTest SuitePrerequisitesYou need to havePythoninstalled.QuickstartUsingpipis the approved way for installingdobson.python3-mpipinstalldobsonTODO Document a use caseLicenseGNU Affero General Public License v3.0LicenseFree software: GNU Affero General Public License v3.0
Package: doc2dash
doc2dashdoc2dashis an extensibleDocumentation Set(docset) generator that helps you to have documentation for all your favorite APIs inDash-compatibleAPI browsers.An API browser is an application that runs locally on your computer and allows you to search various API docs by pressing a key combination and starting to type (I have bound it to ⌥Space bar and can’t write code without it).The most common ones –Dash(macOS) andZeal(Windows and Linux) – come withmanydocsets out of the box, but they can never offer you docsets foreverypackage you’ll ever use.
This is wheredoc2dashcomes in:
It takes your offline documentation and converts it into an indexed format that API browsers can read and search (very fast!).Currently it supports all knownintersphinx-based documentation systems likeSphinx,pydoctor, orMkDocs(withmkdocstrings).
Whiledoc2dashis a Python project, the support isnotlimited to Python-related formats.It’s also extendable:youcan write your own parser!QuickstartThe fastest way to get started is installingdoc2dashfrom ourHomebrew tap:$brewinstallhynek/tap/doc2dash[!NOTE]
If you can’t – or won’t – useHomebrew, check out ourinstallation instructionsfor alternatives.Then pointdoc2dashat a directory containingbuiltintersphinx-compatibledocumentation:$doc2dashdirectory/to/documentationYou should see an output like in the video above and find an appropriately nameddocsetin the current directory.
That’s it – you can add it to Dash or Zeal!doc2dashhas many more features and options – make sure to check out theusagefor more details.
A full how-to isavailable too.Creditsdoc2dashis maintained by Hynek Schlawack and released under theMITlicense.The development is kindly supported by my employerVariomedia AGand all my amazingGitHub Sponsors.Release InformationAddedSupport for high resolution icons using the--icon-2xoption.#200Support for linking todocset playgroundsusing the--playground-urloption.#201Control overfull-text searchusing the--full-text-search=(on|off|forbidden)option.#202FixedThe table of contents-generation forpydoctor-based documentation has been restored.#133
Package: doc2data
doc2dataAbout doc2datadoc2data is a Python library that provides functionality to train deep learning models for various document processing tasks.Currently, models can be trained for four tasks:Page rotationPage croppingDocument (multi-page) classificationToken classificationPlease note that doc2data is currently in a prototype stage.Installationpip install doc2dataDocumentationThe documentation can be foundhere.Licensedoc2datais distributed under the terms of theApache-2.0license.Credits
Package: doc2docx
doc2docxConvertdoctodocxon Windows or macOS directly using Microsoft Word (must be installed).On Windows, this is implemented viawin32comwhile on macOS this is implemented viaJXA(Javascript for Automation, aka AppleScript in JS).InstallVia brew:brew install cosmojg/tap/doc2docxViapipx:pipx install doc2docxVia pip:pip install doc2docxCLIusage: doc2docx [-h] [--keep-active] [--version] input [output]

Example Usage:

Convert single doc file in-place from myfile.doc to myfile.docx:
    doc2docx myfile.doc

Batch convert doc folder in-place. Output docx files will go in the same folder:
    doc2docx myfolder/

Convert single doc file with explicit output filepath:
    doc2docx input.doc output.docx

Convert single doc file and output to a different explicit folder:
    doc2docx input.doc output_dir/

Batch convert doc folder. Output docx files will go to a different explicit folder:
    doc2docx input_dir/ output_dir/

positional arguments:
  input          input file or folder. batch converts entire folder or convert
                 single file
  output         output file or folder

optional arguments:
  -h, --help     show this help message and exit
  --keep-active  prevent closing word after conversion
  --version      display version and exitLibraryfromdoc2docximportconvertconvert("input.doc")convert("input.doc","output.docx")convert("my_doc_folder/")See CLI docs above (or indoc2docx --help) for all the different invocations. It is the same for the CLI and python library.Jupyter NotebookIf you are using this in the context of jupyter notebook, you will needipywidgetsfor the tqdm progress bar to render properly.pip install ipywidgets
jupyter nbextension enable --py widgetsnbextensionAcknowledgementsMany thanks to@AlJohrifor the excellentdocx2pdfupon which this is based!
Package: doc2graph
Doc2GraphThis library is fork development fromhttps://github.com/andreagemelli/doc2graph.pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url &&
https://download.pytorch.org/whl/cu113 &&
pip install dgl-cu113 dglgo -f https://data.dgl.ai/wheels/repo.html &&
pip install setuptools-git-versioning && pip install -e . &&
pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.3.0/en_core_web_lg-3.3.0.tar.gz
Package: doc2map
Doc2MapDoc2Map is an algorithm fortopic modeling and visualization. It can read any type of document files, but not OCR them. It will find topics base on the core idea ofTop2Vecand hierarchicaly display them on a map similar to a Google Map:Live Demo 1 With Wikipedia DatasetLive Demo 2 With 20 News GroupsOr on a scatter plot with a munual zoom level:Live Demo 1 With Wikipedia DatasetLive Demo 2 With 20 News GroupsWhy use Doc2Map?With Doc2Map, you will be able to create beautiful, intuitive, and interactive visuals to summarise your document corpus in a map, similar to Google Map, with topics, clusters, and documents, instead of the names of countries, states, and cities.Thanks to Apache Tika â€“a software able to detect and extract and text from over a thousand different file typesâ€“ allow Doc2Map to read virtually any kind of file.Note:This is not OCR, canâ€™t extract text from pictures.Using Doc2MapThere are two ways to use Doc2Vec:Launching directly the python moduleImporting the Doc2Map library in your scriptLaunching Doc2Map ModuleYour first option is to directly launch the module. Once launch, you will have to wait a little for the programm to start, then you will be asked what folder you want to analyse:Select the folder with the document you want to cartography.For the next step, you will have to be patient. Doc2Map will analyse and convert into plain text your docuemnt, then organise them. Depending of the format, the size and the number of documents, it may take a long time...When finished, two web pages will be automaticaly launch on your browser to show you different cartographies of you documents.The examples are loaded from HTML files newly created. You can easily find their localization by looking at the address bar of your browser, you will see something likefile://Your/Path/To/Your/VisualsThese files can easily be exported to another machine, with little of requirements:If your visualization is based on local files, once exported, these files may no longer be accessible by interacting with the visualisation.However, there will be no problem, if you use a common share hard drive with the people you share the visualisations (like it may often be the case in many firms, under the form of a local network).
For the visualisation DocMap.html, you will have to include the files: DocMapdensity.svg and data.js.Importing in a Python ScriptIf you want to use Doc2Map with python, you have first to install it:pip install Doc2MapThen, you will have to import it:fromDoc2MapimportDoc2MapHow Does It Work?Doc2Map is mainly based on the Top2Vec principle, and rely on Plotly and Leaflet to create beautiful visuals.If you want to know the complete story and working of Doc2Map, I invite you to read the Medium Article about it:
Package: doc2md
Very lightweight docstring to Markdown converter.LicenseCopyright © 2013 Thomas Gläßlet_glaessle@gmx.deThis work is free. You can redistribute it and/or modify it under the
terms of the Do What The Fuck You Want To Public License, Version 2, as
published by Sam Hocevar. See the COPYING file for more details.This program is free software. It comes without any warranty, to the
extent permitted by applicable law.DescriptionLittle convenience tool to extract docstrings from a module or class and
convert them to GitHub Flavoured Markdown:https://help.github.com/articles/github-flavored-markdownIts purpose is to quickly generateREADME.mdfiles for small
projects.APIThe interface consists of the following functions:doctrim(docstring)doc2md(docstring, title)You can run this script from the command line like:$doc2md.py[-a][--no-toc][-ttitle]module-name[class-name]>README.mdLimitationsAt the moment this is suited only for a very specific use case. It is
hardly forseeable, if I will decide to improve on it in the near future.
Package: doc2md2
doc2md本意是为了像倒序forcolorinsorted(colors,reverse=True):printcolors自定义排序顺序colors=['red','green','blue','yellow']defcompare_length(c1,c2):iflen(c1)<len(c2):return-1iflen(c1)>len(c2):return1return0printsorted(colors,cmp=compare_length)更好的方法printsorted(colors,key=len)第一种方法效率低而且写起来很不爽。另外，Python3已经不支持比较函数了。调用一个函数直到遇到标记值blocks=[]whileTrue:block=f.read(32)ifblock=='':breakblocks.append(block)变成 下面 markdown 格式# 倒序forcolorinsorted(colors,reverse=True):printcolors自定义排序顺序colors=['red','green','blue','yellow']defcompare_length(c1,c2):iflen(c1)<len(c2):return-1iflen(c1)>len(c2):return1return0printsorted(colors,cmp=compare_length)更好的方法printsorted(colors,key=len)第一种方法效率低而且写起来很不爽。另外，Python 3已经不支持比较函数了。调用一个函数直到遇到标记值blocks=[]whileTrue:block=f.read(32)ifblock=='':breakblocks.append(block)运行文件是main/doc2md.py一步一步过程 可以使用jupyter notebook查看doc2md.ipynb首先说明缺陷就是，命令行还没有做 请自己进入文件修改先。Path="../让你的Python优雅.md"write_file='../test_w.md'demo 就是useag:$doc2md2file1file2file2.mdiscreatemarkdownfromfile1.md$doc2md2file1file1.mdiscreatemarkdownfromfile1获取``让你的Python优雅.md``md格式\``写``\入``test_w.md``
Package: doc2python
doc2pythonExtracts the text from .doc files as a string. This Project is early in development and only has very limited functionalityInstallationpipinstalldoc2pythonUsefromdoc2pythonimportreadertext=reader.toString('path/to/file.doc')'doc2python' reads the UTF-8 encoded bitstream contained in the file and converts it to a readable string.
At this point in time some special characters are not supported and metadata might get extracted alongside the text.Roadmapsupport for more special charactersadd a parameter, which allows for user input of byte -> character conversion sheets
Package: doc2sdo
doc2sdoExtract entities from text documents and transform them to schema.org resources.Installationpip install doc2sdoUsageFrom the command linedoc2sdo path/to/your.pdfFrom Pythonfrom pathlib import Path
import sys

from doc2sdo import doc2sdo

for thing in doc2sdo(Path("/path/to/your.pdf")):
    thing.resource.graph.serialize(sys.stdout.buffer)DevelopmentPrerequisitesPython 3.10+Python PoetryInstall dependenciesscript/bootstrapRun testsscript/test
Package: doc2term
doc2termA fast NLP tokenizer that detects sentences, words, numbers, urls, hostnames, emails, filenames, and phone numbers. Tokenize integrates and standardize the documents, remove the punctuations and duplications.Installationpip install doc2termCompilationThe installation requires to compile the original C code usinggcc.UsageExample notebook:doc2termExample>>>importdoc2term>>>doc2term.doc2term_str("Actions speak louder than words. ... ")"Actions speak louder than words .">>>doc2term.doc2term_str("You can't judge a book by its cover. ... from thoughtcatalog.com")"You can't judge a book by its cover . from thoughtcatalog.com"
Package: doc2text
doc2textextracts higher quality text by fixing common scan errorsDeveloping text corpora can be a massive pain in the butt. Much of the text data we are interested in as scientists are locked away in pdfs that are poorly scanned. These scans can be off kilter, poor resolution, have a hand in them… and if you OCR these scans without fixing these errors, the OCR doesn’t turn out so well.doc2textwas created to help researchers fix these errors and extract the highest quality text from
their pdfs as possible.doc2textis super duper alpha atmdoc2textis developed and tested on Ubuntu 16.04 LTS Xenial Xerus. We do not pretend to serve all operating systems at the moment because that would be irresponsible. Please use this software with a huge grain of salt. We are currently working on:Increasing the responsiveness of the text block identifier.Optimizing the binarization for tesseract detection.Identifying text in multiple columns (right now, treats as one big column).Handling tables.Many other optimizations.Support and ContributionsIf you have feedback or would like to contribute,please, pleasesubmit a pull request or contact me atjoseph dot sutherland at columbia dot edu.InstallationTo install thedoc2textpackage, simply:pipinstalldoc2textdoc2textrelies on theOpenCV,tesseract, andPythonMagicklibraries. To execute the quick-install script, which installs OpenCV, tesseract, and PythonMagick:curlhttps://raw.githubusercontent.com/jlsutherland/doc2text/master/install_deps.sh|bashManual installationTo install OpenCV manually:sudoapt-getinstall-ybuild-essentialsudoapt-getinstall-ycmakegitlibgtk2.0-devpkg-configlibavcodec-devlibavformat-devlibswscale-devsudoapt-getinstall-ypython-devpython-numpylibtbb2libtbb-devlibjpeg-devlibpng-devlibtiff-devlibjasper-devlibdc1394-22-devgitclonehttps://github.com/opencv/opencv.gitopencvgitclonehttps://github.com/opencv/opencv_contrib.gitopencv_contribcdopencvgitcheckout3.1.0cd../opencv_contribgitcheckout3.1.0cd../opencvmkdirbuildcdbuildcmake-DCMAKE_BUILD_TYPE=RELEASE-DCMAKE_INSTALL_PREFIX=/usr/local-DINSTALL_C_EXAMPLES=OFF-DINSTALL_PYTHON_EXAMPLES=ON-DOPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules-DBUILD_EXAMPLES=ON..make-j4sudomakeinstallsudoldconfigTo install tesseract manually:sudoapt-getinstalltesseract-ocrTo install PythonMagick manually:sudoapt-getinstallpython-pythonmagickExample usageimportdoc2text# Initialize the class.doc=doc2text.Document()# You can pass the lang (as 3 letters code) to the class to improve accuracy# On ubuntu it requires the package tesseract-ocr-$lang$# On other OS, see https://github.com/tesseract-ocr/langdatadoc=doc2text.Document(lang="eng")# Read the file in. Currently accepts pdf, png, jpg, bmp, tiff.# If reading a PDF, doc2text will split the PDF into its component pages.doc.read('./path/to/my/file')# Crop the pages down to estimated text regions, deskew, and optimize for OCR.doc.process()# Extract text from the pages.doc.extract_text()text=doc.get_text()Big thanksdoc2text would be nothing without the open-source contributions of:@danvk@jrosebr1Countless stackoverflow posts and comments.
Package: docai-py
Butler DocAIDocAI helps developers quickly build document, image and text processing pipelines using open source and cloud-based machine learning models for a wide range of applications.🚧 DocAI is still a work-in-progress and undergoing early development.Our goal is to put modern machine-learning technology in the hands of the 20+ million developers in the world. If this excites you, we are looking for early adopters to come along for the ride!RequirementsPython >= 3.7Installation & UsageTo install DocAI with pip:pipinstalldocai-pySystem DependenciesMacInstallpopplerLinuxInstall poppler-utils via your package managerGetting StartedPlease follow theinstallation procedureand then run the following:fromdocaiimportPredictionClient# Get API Key from https://docs.butlerlabs.ai/reference/uploading-documents-to-the-rest-api#get-your-api-keyapi_key='<api-key>'# Get Queue ID from https://docs.butlerlabs.ai/reference/uploading-documents-to-the-rest-api#go-to-the-model-details-pagequeue_id='<queue_id>'# Path to a local JPEG, PNG, or PDF filelocal_file_path='example.pdf'extraction_results=PredictionClient(api_key).extract_document(queue_id,local_file_path)print(extraction_results)MaintainInstall Packages for DevelopmentInstallpoetryon your host machinepoetryinstallButler REST API CodegenTo regenerate code updates to REST API:openapi-python-clientupdate--urlhttps://app.butlerlabs.ai/api/docs-json--configcodegen.yamlRunning Unit TestsTo run all unit tests:poetryrunpytest-v-munit_testsAlternatively, you can also use VSCode's "Testing" tab to run/debug individual testsAdding a New DependencyTo add a new pip package dependency, seepoetry add.For versioning, it is best to use the minimum version that works, combined with^,~, or>=and<checks.For example:poetry add my-package@^1.2.3is a shorthand for>=1.2.3,<2.0.0poetry add my-package@~1.2.3is a shorthand for>=1.2.3,<1.3.0poetry add "my-package>=1.2.3,<4.5.6"For development only dependencies, make sure to include the--devflag.Build and PublishBuild and Publish Setup# setup for testpypipoetryconfigrepositories.testpypihttps://test.pypi.org/legacy/

poetryconfigpypi-token.testpypi<testpypitoken># setup for pypipoetryconfigrepositories.pypihttps://upload.pypi.org/legacy/

poetryconfigpypi-token.pypi<pypitoken>Build and Publish ProcedureUpdatepyproject.tomlanddocai/__init__.pyto have a new version number# build packagespoetrybuild# upload to test pypipoetrypublish-rtestpypi# test install from test pypipipinstall--index-urlhttps://test.pypi.org/simple/--extra-index-urlhttps://pypi.org/simpledocai-py# upload to real pypipoetrypublish-rpypi
Package: do-calculus
do-calculusA Python implementation of thedo-calculusof Judea Pearl et. al.ResourcesDocumentation / Wiki:github.com/bradendubois/do-calculus/wikiSource Code:github.com/bradendubois/do-calculusPyPI:pypi.org/project/do-calculus/Releases:github.com/bradendubois/do-calculus/releasesBug reports:github.com/bradendubois/do-calculus/issuesContact:braden.dubois@usask.caSee thewikito get started.Development StatusA full overhaul has been completed, and marks important milestones in my life and (consequently) this project's. Development on this project is halted until further notice, barring further changes to my own life, or necessary bug fixes and/or security fixes.AcknowledgementsThis project represents approximately two years of part- and full-time work as part of an indescribably fulfilling undergraduate research project. This project was done under the supervision of Dr. Eric Neufeld from approximately Spring 2020 - Winter 2022. Without Dr. Neufeld's support, guidance, patience, expertise (and of course, funding), this project would never have been started, let alone completed. I cannot overstate my appreciation - you've changed my academic and professional path, and provided me with so many wonderful experiences and memories that I will never forget. Thanks, Eric.