Saravanan666/pypi-org huggingface dataset:
Package: autocleus
AutocleusA demi god amongst CLIs: A CLI for creating your own CLIs in Python Easily. Autocleus provides the core engine for your CLI, allowing you to focus on building out the functionality of your CLI without the concern of actually building a CLI. When you finished developing your CLI, you can then package it up using Autocleus. Let Autocleus be of service to you.QuickstartYou can install the most recent version of autocleus from the PyPi repository:pip3 install autocleusAutocleus requirements:Pydantic==1.7.3typing==3.7.4.3PyInquirer==1.0.3virtualenv==20.4.4art==5.1jinja2==2.10.1setuptools==45.2.0pip==20.0.2asciimatics==1.12.0yaspin==1.5.0yamldirs==1.1.8Also need to install python3-venv (Ubuntu, apt-get install -y python3-venv)Please note that this is still under development and has not been properly unit tested.Once you've installed autocleus, the CLI will be accessible from your environment. Currently there is only 1 subcommandgeneratewith two options:--custom_cli: Will launch the interactive form for starting an autocleus CLI project, which is contained within a virtual environment.--concretized_cli: Will launch the interactive form to package an autocleus CLI project into an installable .whl file and a .tar archive (if desired). This will also produce a CLI spec, which is an sqlite database that stores all information about your package, including all of the building blocks of your package. This is meant to be used in an upcoming feature to allow any number of autocleus CLI projects to be combined into a single "master" CLI.Here we will go through the two options for thegeneratesubcommand.autocleus generate --custom_cliQuestionsTasks: Use space bar or click to select what tasks you would like performed. While all are suggested, only those required are selected by default.autocleus generate --concretized_cliwork in progressQuestionsTasks: Use space bar or click to select what tasks you would like performed. While all are suggested, only those required are selected by default.Developing a CLI with AutocleusTODOsFinish READMEThe main parser help function does not work (i.e --help-all or --help)Add feature to output responses to interactive form to YAML and allow autocleus to auto-detect such YAMLs or allow build from such YAMLs (i.e non-interactive mode).Way to many redundant (lazy) imports across package - needs to be cleaned up.More error catching needs to be added in many places (when creating files, directories, etc...).Write real unit tests and setup GitHub actions for CI/CD - current codes in 'tests' directory are just me trying to get things to work :)Known IssuesAutocleus depends on python-virtualenv library: On Unbuntu 20.04 sometimes it is required that you remove and re-install this.sudo apt-get remove python3-venvsudo apt-get install -y python3-venv
Package: auto-click
AutoClick 📱파이썬용 오토클릭 라이브러리입니다.https://pypi.org/project/auto-click/https://github.com/cord0318/python_auto_click다운로드 📥이 모듈은 파이썬 모든 버젼에서 사용 가능합니다.윈도우나 리눅스에서 다음과 같이 입력합니다.pip install auto-click오류가 나는 경우, python -m pip install --upgrade pip 로 pip를 업데이트 해주세요.사용법 🤖fromautoclickimportAutoClickAutoClick(right_key="<ctrl>+v",left_key="<ctrl>+x",delay=0.03)# Default Value: right_key="v", left_key="x", delay=0.04Update기능을 더 쉽게 만들었습니다.
cmd 명령어 기능을 만들었습니다.Tip이제 오토클릭을 cmd에서 사용가능합니다!auto --help를 쳐서 사용가능합니다!
Package: autoclicker
AutoclickerA simple project to auto control mouse.Installpip install autoclickeryou might need some pyautogui dependencies different from the system:https://pyautogui.readthedocs.io/en/latest/install.html#installationPrepare and install whl file (developer mode)make venvcreates virtualenv which allow use autoclicker in developer mode.make install_dependenciesto install required dependencies in system linuxmake create_whlcreates whl file indist/to install autoclicker just run:pip install <name>.whlUsageautoclicker scrolluses middle mouse button to scroll up or downOptions:-s (--scrolls) integer, required parameter which set how many times click middle button and in which direction, e.g.:-s 10,-s -10autoclicker clickeruses left mouse buttonOptions:-s (--screen_location) - by default if this option is not set location is taken from current mouse position e.g.:-s 500 450-r (--repeats) - how many times script should click, e.g.:-r 10-t (--time_break) - average time breaks between clicks in seconds e.g.:-t 15-d (--time_different) - time deviation from the average, e.g.:-t 10 -d 2, real time will be between 8 and 12 seconds
Package: autoclickerPy
AutoclickerSimple autoclicker in python.How to useRunning the autoclicker for some timefromautoclickerPyimportAutoclickerfrompynput.mouseimportButtonclicker=Autoclicker(delay=0.1)# Runs autoclicker for 1 secondclicker.run(1)Starting and stoping the autoclickerfromautoclickerPyimportAutoclickerfrompynput.mouseimportButtonimporttimeclicker=Autoclicker(delay=0.1)clicker.start(button=Button.middle)...somecode...clicker.stop()
Package: auto-cli-py
auto-cli-pyPython Library that builds a complete CLI given one or more functions.Most options are set using introspection/signature and annotation functionality, so very little configuration has to be done.SetupTL;DR Install for usage# Install from githubpipinstallauto-cli-py# See example code and outputpythonexamples.pyIn python codeDevelopmentStandard python packaging - Follows methodologies from:https://python-packaging.readthedocs.io/en/latest/minimal.htmlUses pytestPytesthttps://docs.pytest.org/en/latest/Python (Anaconda) environment(assumes anaconda is properly installed)# First time. Create conda environment from environment.yml and activate it:condaenvcreate-fenvironment.yml-nauto-cli-py
condaactivateauto-cli-py# If environment changes:condaactivateauto-cli-py
condaenvupdate-f=environment.yml# -- OR remove and restart --condaremove--nameauto-cli-py--all
condaenvcreate-fenvironment.ymlActivate environmentcondaactivateauto-cli-py# This symlinks the installed auto_cli package to the source:pipinstall-e.Preparationcondaactivateauto-cli-pyLinting and Testingpytest behavior and output is controlled throughauto_cli/tests/pytest.ini# Lint all code:pylintauto_cli# Run all testspytest# See more options for pytest:pytest--help# This is handy:pytest--fixtures-per-testInstallation (other)# AND/OR Install from a specific github branchpipuninstallauto-cli-py
pipinstallgit+https://github.com/tangledpath/auto-cli-py.git@features/blah---## Example#!/usr/bin/env python"""Simple Examples of CLI creation."""importsysfromauto_cli.cliimportCLIimportenumdeffoo():print("FOO!")deftrain(data_dir:str='./data/',initial_learning_rate:float=0.0001,seed:int=2112,batch_size:int=512,epochs:int=20):print("Training with initial_learning_rate:{initial_learning_rate}, seed:{seed}, batch_size:{batch_size}, epochs:{epochs}into data_dir:{data_dir}")#AnimalEnum = enum.Enum('Animal', 'ANT BEE CAT DOG')classAnimalEnum(enum.Enum):ANT=1BEE=2CAT=3DOG=4defcount_animals(count:int=20,animal:AnimalEnum=AnimalEnum.BEE):returncountif__name__=='__main__':fn_opts={'foo':{'description':'Foobar'},'train':{'description':'Train'},'count_animals':{'description':'Count Animals'},}cli=CLI(sys.modules[__name__],function_opts=fn_opts,title="Foobar Example CLI")cli.display()```
Package: auto-clockify
AutoClockifyThis is a simple python command line utility with which you can automate calling the Clockify API to start and stop the timer.UsageStart the timer:auto_clockify --config=/path/to/config.json startStop the timer:auto_clockify --config=/path/to/config.json stopConfigurationThe minimum configuration information you must supply in the JSON formatted configuration file is:{
  "base_url": "https://api.clockify.me/api/v1",
  "api_key": "_your_api_key",
  "user_id": "_your_user_id",
  "workspace_id": "_your_worspace_id"
}
Package: autocloudreporter
autocloudreporteris a fedmsg consumer for reporting Autocloud
results to ResultsDB. It listens out for Autocloud fedmsg messages, and
submits results using the resultsdb_conventions module for conveniently
reporting results in ‘conventional’ format.The code was written with Python 3 in mind, but it turns out that
verification of fedmsg message signatures does not currently work in
Python 3, so it should be run under Python 2 for now.RequirementsPython libraries:fedmsgfedfindresultsdb_apiInstallationInstall the required external Python libraries, then use setuptools to
install, e.g.:python setup.py installTest and production modesTwo consumers are provided, a ‘test’ and a ‘production’ consumer. For
‘test’:The consumer listens fordev(notprod) messagesThe consumer does not validate message signaturesThe consumer reports to a ResultsDB instance running on localhost
port 5001In this mode it is safe to play around with the consumer, and you can
use a tool likefedmsg-dg-replayto trigger event creation by
replaying a relevant fedmsg (which will show up with adevtopic
rather thanprod).For ‘production’:The consumer listens forprod(notdev) messagesThe consumer validates message signaturesThe consumer reports to the production ResultsDB instance (if
permitted)PLEASEdo not enable the production consumer on a system authorized
to submit results to ResultsDB without checking with Fedora QA.The fedmsg config keys for the consumers areautocloudreporter.test.enabledandautocloudreporter.prod.enabledrespectively.Licenseautocloudreporteris released under the GPL, version 3 or later. SeeCOPYINGand the header ofautocloudreporter.pyitself.
Package: autocluster
autoclusterautoclusteris an automated machine learning (AutoML) toolkit for performing clustering tasks.Report and presentation slides can be foundhereandhere.PrerequisitesPython 3.5 or aboveLinux OS, orWindows WSLis also possibleHow to get started?First, installSMAC:sudo apt-get install build-essential swigconda install gxx_linux-64 gcc_linux-64 swigpip install smac==0.8.0pip install autoclusterHow it works?autoclusterautomatically optimizes theconfigurationof a clustering problem. Byconfiguration, we meanchoice of dimension reduction algorithmchoice of clustering modelsetting of dimension reduction algorithm's hyperparameterssetting of clustering model's hyperparametersautoclusterprovides 3 different approaches to optimize the configuration (with increasing complexity):random optimizationbayesian optimizationbayesian optimization + meta-learning (warmstarting)Algorithms/Models supportedList of dimension reduction algorithms insklearnsupported byautocluster's optimizer.List of clustering models insklearnsupported byautocluster's optimizer.ExamplesExamples are available in thesenotebooks.Experimental resultsThis dataset comprises of 16 Gaussian clusters in 128-dimensional space withN = 1024points. The optimal configuration obtained byautocluster(SMAC + Warmstarting) consists of a Truncated SVD dimension reduction model + Birch clustering model.This dataset comprises of 15 Gaussian clusters in 2-dimensional space withN = 5000 points. The optimal configuration obtained byautocluster(SMAC + Warmstarting) consists of a TSNE dimension reduction model + Agglomerative clustering model.LinksLinkto pypi.Greatwriteupby Martin Krasser on Bayesian OptimizationDisclaimerThe project is experimental and still under development.
Package: auto-clustering
auto-clusteringAutomatic Clustering selection with Ray TuneImportant Note:This tool may optimize metrics such as the validity index, silhouette or the davies-bouldin score,
but as this is based on unsupervised learning, these metrics may not always reflect the true usefulness
of the resulting clusters.Example: Clustering SelectionfromautoclusteringimportAutoClusteringfromsklearn.datasetsimportload_digitsdata,_=load_digits(return_X_y=True)clustering=AutoClustering(num_samples=50,metric='validity_index',n_jobs=-1,verbose=0)clustering.fit(data)clustering.best_params_clustering.best_score_clustering.n_clusters_clustering.best_estimator_clustering.predict(data)ChangelogSee thechangelogfor notes on the changes of auto-clusteringImportant linksOfficial source code repo:https://github.com/rodrigo-arenas/auto-clustering/Download releases:https://pypi.org/project/auto-clustering/Issue tracker:https://github.com/rodrigo-arenas/auto-clustering/issuesStable documentation:https://auto-clustering.readthedocs.io/en/stable/Source codeYou can check the latest development version with the command:git clone https://github.com/rodrigo-arenas/auto-clustering.gitInstall the development dependencies:pip install -r requirements.txtCheck the latest in-development documentation:https://auto-clustering.readthedocs.io/en/latest/ContributingContributions are more than welcome!There are several opportunities on the ongoing project, so please get in touch if you would like to help out.
Make sure to check the current issues and also
theContribution guide.Big thanks to the people who are helping with this project!TestingAfter installation, you can launch the test suite from outside the source directory:pytest autoclustering/DisclaimerThe library is still experimental and under heavy development
Package: autocml
AutoCMLAuthenticationThis library (and associated scripts) supports authentication via environment variables. This library supports a variety of different variable names, meant to suit the different names Cisco already uses.PurposeThis LibraryVIRL LibraryBreakout ToolExampleCML ControllerCML_HOSTVIRL2_URLBREAKOUT_CONTROLLER192.168.0.50UsernameCML_USERVIRL2_USERBREAKOUT_USERNAMEadminPassword (Base-64)CML_PASS64cGFzc3dvcmQ=PasswordCML_PASSVIRL2_PASSBREAKOUT_PASSWORDpasswordAny combination of the above variables can be used, although the leftmost variable name will be prioritized in case of conflicts. UsingCML_PASS64is recommended (and prioritized over other password variables) to prevent the likelyhood of successful shoulder-surfers.Bash exampleCML_HOST="<cml server hostname/IP address>"CML_USER="admin"CML_PASS="password"Note: Above example uses a plaintext password. I first recommend converting it to base64, then passing it in aCML_PASS64environment variable.InstallingThe following should work in both Linux/Bash, and PowerShell.pipinstallvirl2-client# Install official VIRL/CML library from Ciscopipinstallautocml# Install this library + associated utilitiesScriptsThis library includes common scripts/commands useful for administrative purposes, or for automating common lab scenarios.cml-add-usersLoads in a CSV file containing a list of users (a sample CSV can be saved by runningcml-add-users --template <OUTPUT TEMPLATE FILENAME>By default, it adds the users in the csv file to the authenticated CML instance. It only adds/affects users that do not already exist.Can also delete users that are listed in the csv, if a--deleteflag is passed.Example template:Username,Password,Full Name,Description,Roles,Groups
user1,plaintext password,User One,The first user,admin,admin_group
user2,another_password,User Two,The second user,,"net378,net123"Example usage:cml-add-users <USER CSV FILENAME>View help info for more details (cml-add-users --help)(Currently untested as the primary author does not have admin access to a multi-user instance)cml-verify-intsAsserts a lab's interfaces against a .csv file. The csv file must have exactly three columns, (device ID/label, interface, and IP address (with optional CIDR notation)) and may look like the following:Node Label,Int,IPv4
n0,GigabitEthernet0/0,192.168.0.24/26
R2,Fa0/2,10.1.34.2/24
R3,L1,10.255.0.3/32One ran, it will print a report showing which interfaces match, or do not match. Interfaces not listed will not be checked.Can also dump a CSV of interfaces, that can later be used with this utility to double-check interface addresses.Also has a flag to emit results as JSON, although it cannot read in JSON inputs.cml-bulk-labsAllows for downloading/uploading/deleting lab YAMLs in bulk for archival purposes. Useful when migrating YAML labs files from one CML controller to another (like for a fresh upgrade).View the help for detailed configuration options/features (cml-bulk-labs --help)cml-dump-cfgs (WIP)cml-dump-cmds (WIP)cml-dump-pings (WIP)cml-pcap (WIP)cml-start-session (WIP)Opens a tabbed terminal emulator to have a tab for each node's console within a lab.
Package: autococonut
AutoCoconut, a workflow recording tool for LinuxAutoCoconutis a tool that enables tracking mouse and keyboard events to make a workflow report with screenshot illustrations.
Such workflow report can be helpful when creating bug reports, tutorials, or test cases for GUI testing frameworks, such as OpenQA
and others.AutoCoconutworks onX11sessions only. The current version isnot Wayland ready.DevelopmentCurrently, the development has reachedPhase 4.Which means that the script is able:record various events,  mouse buttons and actions (click, double click, drag, vertical scroll), keyboard events (press and release)identify various types of keys (modifiers, special keys, character keys, etc.)find pre-defined patterns in single events and interpret themtake screenshots to illustrate the workflow (or create needles for OpenQA)produce various output -rawfile,jsonfile, or a workflow description in adoc and html.it has a GUI version which brings more functionality, such as edit, delete, or create events for the recorded workflowis packaged on PyPi for easy installation.How to install?The script is being developed and tested on Fedora, so the following procedure is related to Fedora. For other distributions, you need to
make sure, that the following requirements are met:Python development packages.Tkinter librariesOn Fedora, you can follow this procedure:Install thepython3-devel.Installpython3-tkinter.Then you can install the application:pip install --user autococonutHow to use?See the documentation in thedocsdirectory of theautococonutpackage.
Package: autocode
This is a structure template for Python command line applications, ready to be
released and distributed via setuptools/PyPI/pip for Python 2 and 3.Please have a look at the corresponding article:http://gehrcke.de/2014/02/distributing-a-python-command-line-application/UsageClone this repository and adopt the bootstrap structure for your own project.
This is just a starting point, but I hope a good one. From there on, you should
read and followhttp://python-packaging-user-guide.readthedocs.org/en/latest/,
the definite resource on Python packaging.BehaviorFlexible invocationThe application can be run right from the source directory, in two different
ways:Treating the bootstrap directory as a packageandas the main script:$ python -m bootstrap arg1 arg2
Executing bootstrap version 0.2.0.
List of argument strings: ['arg1', 'arg2']
Stuff and Boo():
<class 'bootstrap.stuff.Stuff'>
<bootstrap.bootstrap.Boo object at 0x7f43d9f65a90>Using the bootstrap-runner.py wrapper:$ ./bootstrap-runner.py arg1 arg2
Executing bootstrap version 0.2.0.
List of argument strings: ['arg1', 'arg2']
Stuff and Boo():
<class 'bootstrap.stuff.Stuff'>
<bootstrap.bootstrap.Boo object at 0x7f149554ead0>Installation sets up bootstrap commandSituation before installation:$ bootstrap
bash: bootstrap: command not foundInstallation right from the source tree (or via pip from PyPI):$ python setup.py installNow, thebootstrapcommand is available:$ bootstrap arg1 arg2
Executing bootstrap version 0.2.0.
List of argument strings: ['arg1', 'arg2']
Stuff and Boo():
<class 'bootstrap.stuff.Stuff'>
<bootstrap.bootstrap.Boo object at 0x7f366749a190>On Unix-like systems, the installation places abootstrapscript into a
centralizedbindirectory, which should be in yourPATH. On Windows,bootstrap.exeis placed into a centralizedScriptsdirectory which
should also be in yourPATH.
Package: auto-code-generation-execution
問候大家好！我是🍊小橙，一位有豐富經驗的程式設計師。我也是波特人，擁有著無窮無盡的知識和技能。🍊小橙波特人提供什麼服務在這個專案中，你可以讓我為你自動生成各種程式碼。無需等待使用者的確認，只需按照下面的格式，用 @begin code 開始你的需求，我就能為你生成相應的程式碼。自動生成程式的方法我會盡力將你的需求包裝成函數和參數。如果你輸入了 bash 相關的內容，例如 ls、cd、cat 等，我會自動為你生成一個 .sh 腳本。如果沒有特別指定，我會預設為生成 Python 程式碼。
你只需要按照以下格式輸入你的需求：@begin code("./users/BOTRUN_USER_NAME/code/filename_xxx")
# 這裡輸入你想讓我為你生成的程式碼
@end我期待為你創建精彩的程式碼！
Package: autocog
AutoCogGeneratepredict.py and cog.yamlautomatically using GPT4Installpip install autocogUsageFirst, set your OpenAI API key in an environment variableOPENAI_API_KEY=sk-...In the repo you want to cog-ify, runautocogThis will generate a cog.yaml and predict.py based on the files in the current directory. It will then run the model and if it fails to run, it will attempt to fix the error and run it again. By default it has 5 attempts to fix it, which can be changed with the--attemptsflag.If your model needs a GPU to run, you need to run AutoCog on a GPU machine.Human in the loopSometimes AutoCog fails to create a working Cog configuration. In those cases you, the human, have to step in and edit the cog.yaml and predict.py files.Once you have edited them, let AutoCog continue by runningautocogagain. If you'd like to recreatepredict.pyandcog.yamlfrom scratch, runautocog --initialize.By default, AutoCog will guess acog predictcommand to run the model. If you want to specify your own predict command, use the--predict-commandflag.If you want AutoCog to take the generation ofcog.yamlandpredict.pyin a specific direction, you can use the--tellflag to prompt GPT4:autocog --tell="Add inputs to allow for inpainting"
Package: autocolorize
autocolorizeAutomatically colorize images using Machine Learning.Project pagearXiv paperInstallationMake sure that you have Caffe (with Python bindings). Then run:pip install autocolorizeRun:autocolorize grayscale.png -o colorized.pngAPIYou can also colorize from Python (assuminggrayscaleis the image that you want to colorize):import autocolorize
classifier = autocolorize.load_default_classifier()
rgb = autocolorize.colorize(grayscale, classifier=classifier)
Package: autocomplete-light
Autocomplete-Light: simple autocompletion web componentDifference with other HTML autocompletion elements:defers the rendering of the select box to the serverlightweight javascript web component with no dependencyWhile it is best suited for server side framework integration, it may also be
used on its own, to create a global navigation input like in the facebook top
bar, as well as to create replacement widgets for HTML selects.DemoClone the repository and runpython serve.pyUsageFor usage, check theindex.html source
codeTestingThis module exposes a Python library to provide an API for Selenium and make
integration testing easier with Python projects.Install the python package with test dependencies:# in repository clone
pip install -e .[test]
python serve.py &
py.test -sv