Saravanan666/pypi-org huggingface dataset:
Package: accern-xyme
accern_xymeis a python/typescript library for accessing XYME functionality.Python UsageYou can installaccern_xymewith pip:pipinstall--useraccern-xymeImport it in python via:importaccern_xymexyme=accern_xyme.create_xyme_client("<URL>",token="<TOKEN>",namespace="default")print(xyme.get_dags())<URL>and<TOKEN>are the login credentials for XYME.You will need python3.6 or later.Typescript DevelopingYou can install dependency withyarn. Runyarn _postinstallto configure husky pre-commit hooks for your local environment.
Package: access-checker-sbhalodia
Access CheckerWhat Is This?This is a simple Python/Flask application intended to check if given access will be allowed or not via Cisco ACL(may contain hundreds of lines!!). This tool will take user inputs(Source IP, Source port, Protocol, Destination IP and Destination port) and will check against the user provided ACL and provide the result. This tool have both CLI and GUI options.How To Install ThisActivate your Python virtual environment by following below stepsRunpip3 install virtualenvCreate a project directory and navigate to itCreate virtual environment by runningvirtualenv -p python3 venvActivate virtual environment by runningsource venv/bin/activateMore infoHow To: Virtual environmentsInstall this package by runningpip3 install access-checker-sbhalodiaHow To Use ThisCLIRun the following command from cliExample:access-checker-cli -sip 10.1.1.10/24 -sport 22 -p tcp -dip 8.8.8.10/32 -dport 443 -f /Users/Mytestaccount/Desktop/myaclfile.aclGUIRunaccess-checker-guiNavigate tohttp://localhost:5000in your browserTestingBest effort testing has been done. No thorough testing is completed. Please conduct your own testing before using this.NotePlease follow the exact input format as suggested in GUI and CLI.
Package: accesschk2df
Automating the retrieval and analysis of access information using accesschk.exepip install accesschk2dfTested against Windows 10 / Python 3.10 / Anaconda 3Individuals or organizations working with access control and security configurations can benefit from using this Python
module by automating the retrieval and analysis of access information using accesschk.exe
and leveraging the flexibility and functionality of Python and pandas for further data processing and analysis.accesschk.exe is a command-line tool developed by Microsoft that is used to view and analyze the security
settings and access permissions of various system resources, such as files, directories, registry keys, services,
and more. It provides detailed information about access control lists (ACLs) and user privileges for specific resources.This module utilizes the accesschk.exe tool to retrieve access information and convert it into a pandas DataFrame.
By using this module, individuals or organizations working with access control and security configurations can
programmatically access and analyze access permissions in a more convenient and automated manner.Advantages of using this Python module include:Automation:
The module allows for the automation of accesschk.exe functionality through Python code,
enabling users to retrieve and process access information programmatically.Integration:
The module integrates the functionality of accesschk.exe with pandas, a popular data manipulation library in Python.
This enables users to easily perform further data analysis, transformations, and visualizations on the access
information using pandas' extensive capabilities.Flexibility:
Python provides a wide range of data analysis and processing libraries, making it easier to integrate the access
information with other data sources and perform complex analyses or combine it with additional security-related tasks.Reproducibility:
By using Python code, users can document and reproduce their access information retrieval and analysis workflows.
This is especially useful for auditing, troubleshooting, or creating reports related to access permissions.df=get_accesschk_df()# print(df[:3].to_string())#    aa_pid       aa_exe aa_rights                      aa_path# 0     592    lsass.exe        RW           NT-AUTORITT\SYSTEM# 1     592    lsass.exe        RW  VORDEFINIERT\Administrators# 2      84  svchost.exe         R  VORDEFINIERT\Administrators
Package: access-client
Access ClientPython client to interact with acessai's various applications/services like access-face-visionInstallationpipinstallaccess-cleintUsagefromaccess_client.face_vision.clientimportAFV#instantiate by giving the host and port of the access-face-vision serverafv=AFV("http://localhost:5001")Now we need to create a face-group. This is will be our face-index.afv.create_face_group(face_group_name="celebrities")#Pass in the directory and face group nameafv.add_faces_to_face_group(dir="./samples/celebrities",face_group="celebrities")# Directory structure# **/Images/#          A/#           A_K_01.jpg#           A_K_02.jpg#          B/#           B_S_01.jpg#           B_S_02.jpgOnce faces are indexed we can run inference on it.afv.parse(img_path="./samples/celebrities/Bill Gates/Bill_Gates.jpg",face_group="celebrities")
Package: accessdb
Why I created this package ?One by one insertion into Access DB, But it takes too much time to insert 100k
records,cursor.execute("INSERT STATEMENT")cursor.commit()Bulk way insertion into Access DB and it is fast if you compare with above one,
But even it is very slow fromhttps://github.com/mkleehammer/pyodbc/issues/120cursor.executemany("Bulk INSERT STATEMENT")cursor.commit()But think if you want to insert 1000k records into AccessDB, how much time you have to
wait?What the package will do ?Imports the data from text file to Access Database.Creating Access Database from pandas dataframe very quickly.Primary Key support.Can create many tables in Access DatabaseData Types supportHow to Use:If you have pandas dataframe you can follow bellow exampleimportaccessdb# your dataframe# df.to_accessdb(<DB_PATH>, <TABLE_NAME>)df.to_accessdb(r'C:\Users\<user>\Desktop\test.accdb','SAMPLE')If you have text file you can follow bellow examplefromaccessdbimportcreate_accessdb# create_accessdb(<DB_PATH>, <TEXT_FILE_PATH>, <TABLE_NAME>)create_accessdb(r'C:\Users\<user>\Desktop\test.accdb',r'C:\Users\<user>\Documents\test.text','SAMPLE')Installation:pip install accessdbNote:It will create text file if you are using pandas dataframe to create Access Database,
But the file will be deleted after completion of process.It supports only for Windows.
Package: access-dict-by-dot
access_dict_by_dotUsing this package, we can access the items in a dictionary using dot operator instead of writiting dict_name["key"].for examplefromaccess_dict_by_dotimportAccessDictByDotdictionary={'key1':'value1','key2':'value2','key3':{'subkey1':'subvalue1','subkey2':'subvalue2'}}d=AccessDictByDot.load(dictionary)print(d.key1)print(d.key3.subkey1)
Package: accesser
AccesserEnglish version一个解决SNI RST导致维基百科、Pixiv等站点无法访问的工具支持的站点使用如果不知道什么是Python从这里下载Windows一键程序，运行既可（建议关闭其他代理软件），首次使用会要求安装证书，选是即可。如果已经安装了Python 3.10*或更高版本pip3 install -U "accesser[doh,doq]"如果你不需要 DNS-over-HTTPS 和 DNS-over-QUIC，则可以不用带[doh,doq]。然后通过如下命令启动：accesser对于Windows系统，默认情况下（没有指定--notsetproxy）会设置PAC代理为http://localhost:7654/pac/?t=<随机数>，如果没有可以手动设置。此外，对于Windows系统，默认情况下（没有指定--notimportca）会自动导入证书至系统，如果没有可以手动导入，请看这里。*可以使用例如pyenv来安装所需的Python版本（推荐Python 3.11+）。设置启动一次Accesser后，会在工作目录下生成config.toml，具体含义见其中注释，保存后重新打开程序。进阶1: 与v2ray等其他代理软件一起使用Accesser是一个本地HTTP代理，默认代理地址为http://localhost:7654，只要网络流量能从其他代理软件以HTTP代理导出就能联合使用。以v2ray为例，可以添加一个HTTP的outbound指向http://localhost:7654，并设置相应的路由规则，将维基百科、Pixiv等站点的流量送到这个outbound。
并在启动 Accesser 时带上--notsetproxy参数以避免 Accesser 设置系统代理。此外，你还可以设置一个DNS outbound，然后编辑config.toml让Accesser使用这一DNS。进阶2: 增加支持的网站编辑工作目录下的pac文件（如果是一键程序，可以从GitHub下载这一文件到工作目录），使要支持的网站从代理过。然而，并不是所有站点都可以直接工作，可能需要一些调节，见如何适配站点。
Package: access-face-vision
Access Face VisionFace detection and recognition Application.With pipInstallationpipinstallaccess-face-visionTraining/Creating FaceGrouppython-maccess_face_vision--modetrain--img_dirsamples/celebrities--face_groupcelebrities# Directory structure# **/Images/#          A/#           A_K_01.jpg#           A_K_02.jpg#          B/#           B_S_01.jpg#           B_S_02.jpgRunning Inferences# Live video feed>python-maccess_face_vision--modelive-video--camera-index0--camera_wait30--face_groupcelebrities# server modepython-maccess_face_vision--modeserverUseaccess-clientto make requests to this serverDocker imageDocker Image builddockerbuild-taccess_face_vision:latest.Docker run# we will use it as root directory for access_face_vision applicationmkdir-paccessai/afv# Start serverdockerrun-v$(pwd)/afv:/accessai/afvpython-maccess_face_vision--modeserver# Start camera feed processordockerrun-v$(pwd)/afv:/accessai/afvpython-maccess_face_vision--modeserverContributionContributions are welcome. Feel free to raise PRs! with any improvements.CreditFace Encoder model:https://github.com/nyoki-mtl/keras-facenet
Package: access-github
Access GitHub緣由與動機🔑關鍵：最關鍵的動機是讓全球百萬開發者，都能免費享受分散運算 20 VMs 加速能力!驅動 Github Actions。
💣地雷：直接去用 gpt-4 開發 github api 會撞牆，寫出一堆與最新文件不相容的程式碼，因此要避開此地雷。而在日常的軟體開發過程中，我們經常需要和 GitHub 進行交互。例如，從不同的存儲庫中讀取文件，更新文件，創建和刪除文件等。雖然我們可以通過 GitHub 網站界面完成這些操作，但在某些情況下，使用命令行或 Python 腳本來進行這些操作會更加方便。
為了解決這些問題，本專案設計了一個簡單可用的 Python 庫和對應的命令行工具，以幫助開發者在不離開終端或編程環境的情況下與 GitHub 進行交互。這將大大提高我們的工作效率，使得開發過程更加流暢。安裝方法通過以下命令安裝access-github：python3 -m pip install access-github命令行工具用法使用python3 -m access_github可以使用命令行工具，下面是具體用法：python3 -m access_github [operation] --token TOKEN --url URL [other_arguments]可用操作get_tree: 獲取指定存儲庫的代碼樹read_file: 讀取指定路徑的文件內容update_file: 更新指定路徑的文件內容create: 創建指定路徑的文件或文件夾delete: 刪除指定路徑的文件或文件夾create_or_update_github_action: 創建或更新 GitHub Actions 工作流配置文件dispatch_github_action: 觸發指定的 GitHub Actions 工作流用法範例：以下是一些常見的用法示例：獲取存儲庫的代碼樹：python3 -m access_github get_tree --token TOKEN --url REPO_URL讀取指定路徑的文件內容：python3 -m access_github read_file --token TOKEN --url REPO_URL --path FILE_PATH更新指定路徑的文件內容：python3 -m access_github update_file --token TOKEN --url REPO_URL --path FILE_PATH --content NEW_CONTENT --name YOUR_NAME --email YOUR_EMAIL創建文件：python3 -m access_github create --token TOKEN --url REPO_URL --path FILE_PATH_OR_FOLDER --content FILE_CONTENT --name YOUR_NAME --email YOUR_EMAIL刪除文件：python3 -m access_github delete --token TOKEN --url REPO_URL --path FILE_PATH創建 GitHub Actions 工作流：python3 -m access_github create_or_update_github_action --token TOKEN --url REPO_URL --path WORKFLOW_YML_FILE_PATH --content WORKFLOW_YML_CONTENT觸發 GitHub Actions 工作流：python3 -m access_github dispatch_github_action --token TOKEN --url REPO_URL --workflow_yml_filename WORKFLOW_YML_FILENAME --event_type EVENT_TYPE --client_payload CLIENT_PAYLOADPython 庫用法可以通過from access_github import FUNCTION_NAME來導入需要使用的功能，如：fromaccess_githubimportget_tree,read_file,update_file,create,delete,create_or_update_github_action,dispatch_github_action函數參考get_tree(token: str, url: str) -> dict: 獲取指定存儲庫的代碼樹read_file(token: str, url: str, path: str) -> dict: 讀取指定路徑的文件內容update_file(token: str, name: str, email: str, url: str, path: str, content: str) -> dict: 更新指定路徑的文件內容create(token: str, name: str, email: str, url: str, path: str, content: str) -> dict: 創建指定路徑的文件delete(token: str, url: str, path: str) -> dict: 刪除指定路徑的文件create_or_update_github_action(token: str, url: str, path: str, content: str) -> dict: 創建或更新 GitHub Actions 工作流配置文件dispatch_github_action(token: str, url: str, workflow_yml_filename: str, event_type: str, client_payload: str) -> str: 觸發指定的 GitHub Actions 工作流函數使用範例以下是使用 Python 庫操作的示例：fromaccess_githubimportget_tree,read_file,update_file,create,deletetoken='your_token'url='https://github.com/user/repo.git'path='path/to/file.txt'# 獲取代碼樹tree=get_tree(token,url)# 讀取文件content=read_file(token,url,path)# 更新文件update_result=update_file(token,'your_name','your_email',url,path,'new_content')# 創建文件create_result=create(token,'your_name','your_email',url,'path/to/new/file','file_content')# 刪除文件delete_result=delete(token,url,'path/to/file')請根據您的需求選擇合適的函數進行操作。
Package: accessi
Siemens Access-i Interface libraryLibrary for Siemens Access-i MR Scanner Interface to integrate and control the MR Scanner. Based on Version 1.1.2 for NX (Access-i Developer Guide)Installpip install accessiSiemens DocumentationThe library is based on this document:Access-i Dev Guide NX V1.1.2Usage guideA sample test suite (tests.py) has been created which demonstrates basic Access-i usage.The tests.py requires Access-i simulator to be running on the background, on the same local computer.The tests.py demonstrates most of the implemented methods, as well as receiving images over websocket.CollaboratingThe majority of Access-i functionality is not yet implemented here, if you need more functionality, any additions are accepted.
Package: accessibility
accessibilityis a Python module that wraps the Accessibility API for Mac OS X. It can be used to query and modify attributes of running applications, as well as watch for a variety of notifications. The source code and several examples (in theexamplesdirectory) are hosted onGitHub.The module should compile under recent versions of both Python 2 and 3, and work with Mac OS X 10.8.x and 10.9.x. In addition, to compile the module on version 10.9.0 or later of OS X, you will need to have the Xcode IDE installed.BuildingThe module can be compiled using the traditionalpython setup.py clean build installprovided by setuptools.DocumentationThe module includes extensive docstrings, complete with examples in many cases. These can be can be browsed using Python’shelpcommand, or one can compile the Sphinx documentation. For the latter:cd docsInitialize the git submodule withgit submodule update--init--recursiveto retrieve the custom Sphinx theme.make htmland then browse the documentation indocs/_build/html.LicenseThis project is under the ISC License. See theLICENSE.txtfile for details.
Package: accessible-graphs
Accessible graphs packageThis package enables you to experience graphs in an accessible manner if you're a blind person who uses a screen reader with or without a braille display.How it works?To get the accessible graph, you need to:Import the package as follows:import accessible_graphs_pkgCall the function "getAccessibleGraph" as follows:accessible_graphs_pkg.getAccessibleGraph(rawData, description, minValue, maxValue)The "getAccessibleGraph function accepts 4 arguments:raw Data - which could be:A list of numerical values representing the graphA dict, where the keys are the labels for the data, and the values are the numbers corresponding to each labeldescription - an optional string describing the graphminValue - an optional argument which tells the system that this is the minimum value. If not specified, the system calculates this value automatically based on the data rangeminValue - an optional argument which tells the system that this is the minimum value. If not specified, the system calculates this value automatically based on the data rangemaxValue - an optional argument which tells the system that this is the maximum value. If not specified, the system calculates this value automatically based on the data rangeExample 1Suppose we want to make the following graph accessible, represented by the following values:1500, 1300, 1700, 2000, 1000, 1450, 1900Suppose also we want to give the description "Demo stock example" to our graph.Then we call our function as follows:accessible_graphs_pkg.getAccessibleGraph([1500, 1300, 1700, 2000, 1000, 1450, 1900], 'Demo stock example')We then are supposed to get a graph similar to the one inthis linkNote that the minimum and maximum values are calculated automatically in this example.Example 2Let's now talk about more realistic example. Suppose we want to get the graph describing the following stock to be accessible, when represented by a list of key-value pairs, where the key is the day in the week, and the value is the value of the stock:Sunday: 1500Monday: 1300Tuesday: 1700Wednesday: 2000Thursday: 1000Friday: 1450Saturday: 1900Suppose also we want to give the description "Demo stock example" to our graph as before.Then we call our function as follows:accessible_graphs_pkg.getAccessibleGraph({'Sunday': 1500, 'Monday': 1300, 'Tuesday': 1700, 'Wednesday': 2000, 'Thursday': 1000, 'Friday': 1450, 'Saturday': 1900}, 'Demo stock example')Then we are supposed to get a graph similar to the one inthis linkNote that the minimum and maximum values are calculated automatically in this example.Example 3Suppose we want to make the graph represented by the following data to be accessible:1, 2, 3, 4, 5Suppose also we want to tell the system that the minimum value should be 2, and the maximum should be 4. Then we call our function as follows:accessible_graphs_pkg.getAccessibleGraph([1, 2, 3, 4, 5], description = 'some description', minValue= 2, maxValue = 4)Some useful linksAccessible Graphs basic guideAccessible Graphs braille tutorial
Package: accessible_output
The accessible_output libraryAuthor:Christopher Toth <Q@Qwitter-Client.net>Date:$Date: 06-27-2011 02:00:00 -0400 (Mon, Jun 27, 2011)Web site:http://www.qwitter-client.net/Copyright:2011ContentsThe accessible_output libraryIntroductionBasic UsageSpeech OutputsBraille OutputsIntroductionAccessible Output provides a standard way for developers to output text in either speech or braille using a preinstalled screen reader.  Using accessible_output makes creating self-voicing applications extremely easy.Basic UsageUsing accessible output is extremely simple:#!/usr/bin/env python
from accessible_output import speech
s = speech.Speaker() #Will load the default speaker.
s.output("The message to speak")Speech OutputsJAWS for WindowsWindow EyesDolphin Screen Readers newer than v11.NVDA 2010.1 or newerSystem Access and System Access To GoMicrosoft sapi 5 speechSpeech DispatcherApple VoiceOverBraille OutputsJAWS for WindowsWindow EyesNVDASystem Access and System Access To Go
Package: accessible-output2
Accessible Output 2 is an MIT licensed library for speaking and brailling through multiple screen readers and other accessibility systems.Accessible Output 2 makes selection of the appropriate speech and Braille output a snap, and also allows the programmer to select and use a specific output, for instance to force speaking through the Microsoft Speech API even if the user has a screen reader loaded.>>>importaccessible_output2.outputs.auto>>>o=accessible_output2.outputs.auto.Auto()>>>o.output("Some text")#attempts to both speak and braille the given text through the first available output>>>o.speak("Some other text",interrupt=True)#Speak some text through the output, without brailling it, and interrupt the currently-speaking text if anyAccessible Output 2 makes it simple to add spoken and brailled notifications to your applications on multiple platforms, facilitating accessibility for the visually impaired and also providing a nice alternative means of providing notifications to a sighted user.Supported Outputs:Speech:JAWS for WindowsNVDAWindow EyesSystem AccessSupernova and other Dolphin productsPC TalkerZDSRMicrosoft Speech APIBraille:JAWS for WindowsNVDAWindow EyesSystem AccessSupernova and other Dolphin products
Package: accessible-output3
Accessible Output 3 is a fork of Accessible Output 2, with fixed mac supportAccessible Output 3 is an MIT licensed library for speaking and brailling through multiple screen readers and other accessibility systems.Accessible Output 3 makes selection of the appropriate speech and Braille output a snap, and also allows the programmer to select and use a specific output, for instance to force speaking through the Microsoft Speech API even if the user has a screen reader loaded.>>>importaccessible_output3.outputs.auto>>>o=accessible_output3.outputs.auto.Auto()>>>o.output("Some text")#attempts to both speak and braille the given text through the first available output>>>o.speak("Some other text",interrupt=True)#Speak some text through the output, without brailling it, and interrupt the currently-speaking text if anyAccessible Output 2 makes it simple to add spoken and brailled notifications to your applications on multiple platforms, facilitating accessibility for the visually impaired and also providing a nice alternative means of providing notifications to a sighted user.Supported Outputs:Speech:JAWS for WindowsNVDAWindow EyesSystem AccessSupernova and other Dolphin productsPC TalkerZDSRMicrosoft Speech APIBraille:JAWS for WindowsNVDAWindow EyesSystem AccessSupernova and other Dolphin products
Package: accessible-pygments
Accessible pygments themesThis package includes a collection of accessible themes for pygments based on different sources.WCAG 2.1 - AAA compliantThe following themes are AAA compliant withWCAG 2.1 criteria for color contrast.a11y-darka11y-high-contrast-darkpitaya-smoothie- Colorblindness friendly.github-light- Colorblindness friendly.github-dark- Colorblindness friendly.github-light-colorblind- Colorblindness friendly.github-dark-colorblind- Colorblindness friendly.github-light-high-contrast- Colorblindness friendly.github-dark-high-contrast- Colorblindness friendly.gotthard-dark- Colorblindness friendly.WCAG 2.1 - AA compliantThe following themes are AA compliant withWCAG 2.1 criteria for color contrast.a11y-lighta11y-high-contrast-lightgotthard-light- Colorblindness friendly.blinds-light- Colorblindness friendly.blinds-dark- Colorblindness friendly.greative- Accessible to most forms of colorblindness and low light settings.For a demo of all our themes pleaseclick here!InstallationOur package is available in both conda and pip via,conda install -c conda-forge accessible-pygmentspip install accessible-pygmentsIf you want to install it directly from source,git clone git@github.com:Quansight-Labs/accessible-pygments.git
cd accessible-pygments
pip install .UsageImport it using the name identifier for the desired theme,from pygments.formatters import HtmlFormatter
HtmlFormatter(style='a11y-light').style
<class 'accessible-pygments.A11yLight'>TestsJust open a terminal and run,python test/run_tests.pyYou will see the results undertest/resultsin html format for each supported theme.AcknowledgementsWe want to thank the following sources for being the source of inspiration of one or more themes that are available in this repository,a11y dark and light syntax highlighting.pitaya smoothie vscode theme.github vscode themes.gotthard vscode themes.blinds vscode themes.greative vscode theme.
Package: accession2taxid
accession2taxid: saving NCBI accession2taxid file to database for queryinginstallationpip install accession2taxidusagesavingaccession2taxid -i prot.accession2taxid.FULLqueryingfromaccession2taxidimportget_scoped_session,Accession2Taxidwithget_scoped_session()assession:session.query(Accession2Taxid).filter_by(accession='A0A0A0MTA4')
Package: accessiontk
accessiontkTools to manage lists of accessions.webmapCreates nice web maps using leaflet.js and vue.js.Usage:Create a csv/tsv with the following columns (colnames flexible):accession,location,datetime,lat,long,location_description
A1,L1,2022-01-01,48.4,2.2,"somebody's field"Collect image data into a structure like$treeimages/
images
├──A1
│├──20220324120033.jpg
│├──20220324120034.jpg
│├──20220324120035.jpg
├──A2│├──20220324120384.jpg
│├──20220324120485.jpg
│├──20220324120480.jpg
│└──20220324120539.jpg
└──L1├──20220324120871.jpg├──20220324120880.jpg├──20220324120821.jpg└──20220324120857.jpgRunaccessiontk-webmap. Set the--*-colnamearguments to match your metadata table, give-i,-o, and-t, and then run it. Seeaccessiontk-webmap --helpfor info
Package: accesskit
AccessKitThese are the bindings to use AccessKit from Python.Documentation for the Rust packages can be foundhere.An example program showing how to integrate AccessKit in a pygame application is availablehere.Building from a Source DistributionIf there are no wheels available for your platform, you will have to build one yourself. You will need to have Rust installed on your system, so that the native libraries can be compiled. Please visitrustup.rsfor instructions on how to proceed.Building from within the repositoryThis project usesmaturinas its build tool. If you need to manually build wheels for development purposes, it is recommended to install it inside a virtual environment. All maturin commands must be issued from this repository's root directory.