Saravanan666/pypi-org huggingface dataset:
Package: autocompletion
autocompletionJupyterlab extension for JupySqlRequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstallautocompletionUninstallTo remove the extension, execute:pipuninstallautocompletionContributingDevelopment 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 autocompletion 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 uninstallpipuninstallautocompletionIn 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 namedautocompletionwithin that folder.Packaging the extensionSeeRELEASE
Package: autoconnect
This module helps in automatic client-server socket connection setup for networked applications.I wrote this module a while ago (5+ years) and though it would be handy
for others. Its a handy auto-socket connection module. I used it
originally in a internet cafe management system. The client app installed
on the PC would start the receiver. It would then wait for the server
to broadcast its URL and on receiving it connect and do its business.I’ve recently had call to want to do this type of thing again so I thought
I’d dust this code off and make into a proper project. I’ll release the
code I’ve got first the improve it as I start using again.
Package: autocontrol
========Overview========* Free software: MIT licenseActivate virtual environment==================Together with the autoCorrection package you will get'tensorflow','toolz','keras','numpy','kopt','scipy','h5py','sklearn','dask','pandas','statsmodels'packages automatically installed, if not present.If you don't wannt to install these packages globally, please use virtual environment.If you have problems with virtualenv, installing using conda may help:(Installation of conda: https://conda.io/docs/user-guide/install/index.html)Make sure you are using python 3.conda create -n mypyth3 python=3.6source activate mypyth3conda install virtualenvactivate new environment in active python 3 environment:virtualenv env-with-autoCorrectionsource env-with-autoCorrection/bin/activateCheck if you are still using python 3:python --versionPackage Installation============::pip install autocontrolDeactivate virtual environment============::deactivateUsage============::#in python:pythonimport autocontrolimport numpy as npcounts = np.random.negative_binomial(n = 20, p=0.2, size = (10,8))sf = np.ones((10,8))corrector = autocontrol.correctors.AECorrector()c = corrector.correct(counts = counts, size_factors = sf)#in R:library(reticulate)autoCorrection <- import("autocontrol")corrected <- autoCorrection$correctors$AECorrector(model_name, model_directory)$correct(COUNTS, SIZE_FACTORS, only_predict=FALSE)Documentation=============https://i12g-gagneurweb.in.tum.de/public/docs/autocontrol/Changelog=========0.0.1 (2017-11-01)------------------* First release on PyPI.
Package: auto-control-tools
auto-control-toolsUseful scripts:install requirements:pip install -r requirements.txt,pip install -r requirements_dev.txtinstall project as package:pip install -e .run typing check:mypy srcrun format check:flake8 srcrun tests:pytestauto generate modules .rst:sphinx-apidoc -o .\docs\source .\src\auto_control_tools\ -fbuild docs locally:sphinx-build -b html .\docs\source\ .\rtd_build\
Package: autocopyright
autocopyrightAutocopyright is a script which was designed to automatically add copyright notices at
the top of source files. It uses jinja2 templates which can be automatically filled with
values pulled frompyproject.tomland other files.ExampleTo run autocopyright you must specify comment sign, directory to traverse, glob patterns
of files to modify and path to license template.autocopyright -s "#" -d autocopyright -g "*.py" -g "*.pyi" -l "./templates/MIT.md.jinja2"TemplatesAutocopyright uses Jinja2 templates to determine content of copyright header. Such
template is loaded from predefined destination and rendered with few special variables
available. Those variables are listed below:now-datetime.datetimeobject holding current time (determined once, at the
beginning of script execution)pyproject- dictionary-like object holding loaded content ofpyproject.tomlfile
loaded from current working directory of script.Template forLGPL-3.0license could look like this:Copyright{{now.year}}{{pyproject.tool.poetry.authors[0]}}This file is part of{{pyproject.tool.poetry["name"]}}.{{pyproject.tool.poetry.repository}}{{pyproject.tool.poetry["name"]}}is free software: you can redistribute it and/or modify it underthe terms of the GNU Lesser General Public License as published by the FreeSoftware Foundation, either version 3 of the License, or (at your option)any later version.{{pyproject.tool.poetry["name"]}}is distributed in the hope that it will be useful, but WITHOUT ANYWARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESSFOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for moredetails.You should have received a copy of the GNU Lesser General Public Licensealong with{{pyproject.tool.poetry["name"]}}. If not, see <http://www.gnu.org/licenses/>.Pre-commit hookTo add this script as pre commit hook, create.pre-commit-config.yamlfile, or append
to existing one, following lines:repos:-repo:https://github.com/Argmaster/autocopyrightrev:"v1.1.0"hooks:-id:autocopyrightargs:[-s,"#",-d,<your-project-source-dir-name>,-g,"*.py",-l,<path-to-license-template>,]Replace<your-project-source-dir-name>with valid name of your project source
directory, for examplesourceorsrc.Replace<path-to-license-template>with path to jinja2 template file containing
license note, eg."./templates/LGPL3.md.jinja2". SeeTemplatessection for example
of template content.
Package: autocord
⚠️ Autocord is currently going through a complete re-write ⚠️An easy to use API wrapper centered around automationAutocord is a Python API wrapper for Discord centered around automation. Using Autocord, you can easily automate the boring stuff using Python. Features include, sending and recieving messages, changing settings, and creating tasks. Autocord helps you easily make selfbots without the hassle of using discord.py and absolutely none of that intents bs. Syntax is pretty straightforward, quite similar to discord py.Installing ⚙️Requires:requests > 2.28.2websocket > 1.5.1pip install websocket_clientasyncio (aiohttp > 3.8.3)pip install autocordFeatures ✨Option to return data as JSON or ObjectEasy selfbot setupEasily customizableUses the discord user APIQuick Example ⚡importautocordclient=autocord.Client('TOKEN')@client.eventasyncdefon_ready():print(f"Logged in as:{client.user}")@client.eventasyncdefon_message(message):ifmessage.content=='ping':awaitmessage.reply('pong')awaitclient.send(message.channel.id,f'Replied to{message.author.username}')client.run()Notes 📜No embed send support (autocord not meant to be used on actual bot accounts)No attachment send support (working on it...)client.create_dm()returns aDmChannelobject. However, occasionally, you can't send messages without solving a captchaEmbed fields don't show
Package: autoCorrection
========Overview========* Free software: MIT licenseActivate virtual environment==================Together with the autoCorrection package you will get'tensorflow','keras','numpy','kopt','scipy','h5py','sklearn','pandas','statsmodels','pytest'packages automatically installed, if not present.If you don't wannt to install these packages globally, please use virtual environment.If you have problems with virtualenv, installing using conda may help:(Installation of conda: https://conda.io/docs/user-guide/install/index.html)Make sure you are using python 3.conda create -n mypyth3 python=3.6source activate mypyth3conda install virtualenvactivate new environment in active python 3 environment:virtualenv env-with-autoCorrectionsource env-with-autoCorrection/bin/activateCheck if you are still using python 3:python --versionPackage Installation============::pip install autoCorrectionDeactivate virtual environment============::deactivateUsage============::#in python:pythonimport autoCorrectionimport numpy as npcounts = np.random.negative_binomial(n = 20, p=0.2, size = (10,8))sf = np.ones((10,8))corrector = autoCorrection.correctors.AECorrector()c = corrector.correct(counts = counts, size_factors = sf)#in R:library(reticulate)autoCorrection <- import("autoCorrection")corrected <- autoCorrection$correctors$AECorrector(model_name, model_directory)$correct(COUNTS, SIZE_FACTORS, only_predict=FALSE)Documentation=============https://i12g-gagneurweb.in.tum.de/public/docs/autocorrection/Changelog=========0.0.1 (2017-11-01)------------------* First release on PyPI.
Package: autocorrect-py
AutoCorrect for PythonThe Python version ofAutoCorrect.Rust -autocorrectRuby -autocorrect-rbGo -autocorrect-goPython -autocorrect-pyNode.js -autocorrect-nodeJavaScript (Browser) -autocorrect-wasmJava -autocorrect-javaInstallation$pipinstallautocorrect-pyUsageimportautocorrect_pyasautocorrectautocorrect.format("Hello你好.")# => "Hello 你好。"autocorrect.format_for("let title = 'Hello你好。'","js")# => "let title = 'Hello 你好。'"result=autocorrect.lint_for("<h1>这是 Heading标题</h1>","html")printresult# => LintResult(filepath='html', lines=[LineResult { line: 1, col: 5, new: "这是 Heading 标题", old: "这是 Heading标题", severity: Error }], enable=true)# Load configautocorrect.load_config('{ textRules: { "你好hello": 0 } }')autocorrect.format("Hello你好.")# => "Hello 你好。"autocorrect.format("你好hello.")# => "你好hello."# Ignorer, if /path/to/workdir contains .autocorrectignore or .gitignoreignorer=autocorrect.Ignorer('/path/to/workdir');ignorer.is_ignored('README.md');
Package: auto-corr-feature-selection
AutoCorrFeatureSelectionAutomatically select the most relevant features based on correlation.How it worksTheAutoCorrFeatureSelectionclass utilizes correlation analysis to automatically select relevant features from a given dataset. Here's a step-by-step overview of how it works:Correlation Matrix:The first step is to calculate the correlation matrix, which measures the pairwise correlation between all features in the dataset. The correlation matrix provides insight into the relationships between the features.sepal.lengthsepal.widthpetal.lengthpetal.widthvarietysepal.length1.0-0.110.870.810.72sepal.width-0.111.0-0.42-0.36-0.42petal.length0.87-0.421.00.960.94petal.width0.81-0.360.961.00.95variety0.72-0.420.940.951.0Threshold-based Selection:Next, the class applies a threshold to the correlation matrix to identify columns with correlations above the specified threshold (for example 0.85). These columns are considered highly correlated and may contain redundant or similar information.sepal.lengthsepal.widthpetal.lengthpetal.widthvarietysepal.length0.87sepal.widthpetal.length0.870.960.94petal.width0.960.95variety0.940.95Selected Columns and Relationships:The selected columns are visually represented, showcasing the relationships between the highly correlated features. This diagram helps visualize the interconnectedness of these features.By following these steps, the AutoCorrFeatureSelection class automates the process of feature selection based on correlation analysis, enabling you to identify and focus on the most informative and non-redundant features in your dataset.ExampleExamples can be found inexamples/.# set up auto correlationauto_corr=AutoCorrFeatureSelection(df)# select low correlated columnsselected_columns=auto_corr.select_columns_above_threshold(threshold=0.85)filtered_df=df[selected_columns]
Package: autocreateapp
AutoCreateApp - Django Project AutomationAutoCreateApp is a Python package that automates the process of creating Django projects with multiple apps and models based on user input. With this tool, you can quickly set up Django projects without manually creating apps, models, and migration files.InstallationYou can install AutoCreateApp via pip:pipinstallautocreateappUsageOnce AutoCreateApp is installed, you can use theautocreateappcommand to create your Django project interactively.autocreateappFollow the prompts to provide the project name, the number of apps to create, app names, model names, and model fields. The package will automatically generate the necessary files and set up the Django project with your specified apps and models.How It WorksAutoCreateApp uses Python's built-inosmodule to create and navigate directories, and it utilizes Django's command-line tools (django-adminandmanage.py) to create projects, apps, and migration files.Theadd_app_to_installed_appsfunction ensures that the newly created apps are added to theINSTALLED_APPSlist in the Django project'ssettings.pyfile.The package also supports interactive model creation, allowing you to specify model names and their respective fields, such as strings (CharField) or integers (IntegerField).ExampleHere's a simple example of how to use AutoCreateApp:# Create a new Django project with two apps and modelsautocreateapp
EnterthenameofyourDjangoproject:myproject
Howmanyappsdoyouwanttocreate?2Enterthenameofyourapp:app1
Enterthenameofyourapp:app2

Enterthenameofyourfirstmodelinapp1:MyModel
Entermodelfields(comma-separated,e.g.,name:str,age:int):name:str,age:int

Enterthenameofyourfirstmodelinapp2:AnotherModel
Entermodelfields(comma-separated,e.g.,title:str,description:str):title:str,description:strAfter the above steps, you'll have a new Django project calledmyproject, with two apps (app1andapp2), each containing one model (MyModelandAnotherModel) with the specified fields.LicenseAutoCreateApp is distributed under the MIT License. See theLICENSEfile for more information.ContributionsContributions are welcome! If you have any suggestions, bug reports, or improvements, please open an issue or submit a pull request.Happy coding! 🚀
Package: autocron
autocronautocronis a Python background task library with no dependencies beside the standard library. It works out of the box with webframeworks like django, pyramid, flask, bottle, tornado or starlette.autocronmakes it easy to delegate long running and recurring tasks to external processes. No hassle with configuration-files.autocronis designed for the vast majority of webapplications that don't need massive scaling. And don't want to add unnecessary dependencies.InstallationFor installation use pip:$ pip install autocronQuickstartautocron provides two decorators:cronthat takes a string incron-format as argument, but accepts also keyword-arguments likeminutesandhours. Anddelayto delegate a long running task to a background process.Here is a simple example how to use autocron with the flask web-framework that can be run with$ flak --app application run:# application.py

    import autocron
    from flask import Flask

    app = Flask(__name__)
    autocron.start("the_flask_app.db")

    @autocron.cron("* * * * *")
    def cronjob():
        # do something from time to time ...

    @autocron.delay
    def do_this_later():
        # time consuming task here ...

    @app.route("/later")
    def later():
        do_this_later()
        return "delayed action triggered"After creating the flaskappinstance callingautocron.start(<databasename>)starts the background workers. Thecrondecoratedcronjob()function will get executed every minute and thedelaydecorateddo_this_later()function gets delegated to the background worker every time the application processes the/laterurl. Terminating the application will shut down the worker processes.More information and examples how to use autocron with other frameworks are at the documentation.DocumentationThe full documentation and release notes are athttps://autocron.readthedocs.org
Package: autocrosswalk
autocrosswalk: A generic approach to crosswalkingThis library automates crosswalks from one dataframe to another.Please contact the authors below if you find any bugs or have any suggestions for improvement. Thank you!Author: Nicolaj Søndergaard Mühlbach (n.muhlbach at gmail dot com, muhlbach at mit dot edu)Code dependenciesThis code has the following dependencies:Python >=3.6pandas >=1.3InstallationThere are no heavy dependencies for this library to work. We have included an example that requires a parquet reader, e.g.,pyarrow,brotli, orfastparquet. One needs to have one of them installed in order to use the example data provided.
Otherwise, go ahead and install bypip install autocrosswalk.Usage# Librariesfromautocrosswalk.mainimportAutoCrosswalkfromautocrosswalk.toolsimportload_example_data# Load example datadata=load_example_data()# Separate into old and new data, i.e., we crosswalk the 'data_from' to 'data_to'data_from=data.loc[data["DB"]=="db_20_0"]data_to=data.loc[data["DB"]=="db_26_1"]# Instantiateautocrosswalk=AutoCrosswalk(n_best_match=3,prioritize_exact_match=True,enforce_completeness=True,verbose=2)# Generate crosswalk filedf_crosswalk=autocrosswalk.generate_crosswalk(df_from=data_from,df_to=data_to,numeric_key=['O*NET-SOC Code'],text_key=['Job title'])# Perform crosswalkdf_updated=autocrosswalk.perform_crosswalk(crosswalk=df_crosswalk,df=data_from,values=["Data Value"],by=['Date','DB','Category','Element ID','Element Name','Element description'])# Check if number of unique keys matchprint(len(df_updated["O*NET-SOC Code"].unique())==len(data_to["O*NET-SOC Code"].unique()))print(len(df_updated["Job title"].unique())==len(data_to["Job title"].unique()))# Now, 'df_updated' has all new keys from 'data_to'!
Package: auto-crud-param
auto_crud_paramAuto Create Retrieve Update Delete ORM Functionality for Param models using SQL alchemy.Installation:Install with pip:pipinstallauto-crud-paramExample Usage:importparamaspmfromsqlalchemyimportcreate_enginefromsqlalchemy.ormimportdeclarative_base,sessionmakerfromauto_crud_paramimportparameterized_to_modelBase=declarative_base()# Example usageclassA(pm.Parameterized):name=pm.String(default='Item A')value=pm.Number(default=0)AModel=parameterized_to_model(A,Base)# Set up the database (for example, using SQLite)engine=create_engine('sqlite:///mydatabase.db')Base.metadata.create_all(engine)Session=sessionmaker(bind=engine)DevelopmentTo run the package locally you need python, git, and poetry.gitclonegit@github.com:longtailfinancial/auto_crud_param.gitcdgit@github.com:longtailfinancial/auto_crud_param.git
poetryinstall
poetryshell
pythonexample.py
Package: autocrypt
As of 2018, Jan 10th this package is empty and has been renamed.  If you
are looking for Autocrypt implementing python code you may rather look
at:https://pypi.python.org/pypi/muacryptwhich offers
a command line tool and documented APIs to manage
Autocrypt-capabilities for Mail User agents (MUAs).
It usesgpgorgpg2under the hood
for crypto operations.https://github.com/juga0/pyacis an example
implementation of Autocrypt Level 1 using thehttps://pypi.python.org/pypi/pgpypackage to
implement PGP operations.For other (upcoming) implementations refer tohttps://autocrypt.org/dev-status.html
Package: autocut3d
3D AutoCuta 3D segmentation algorithm based on cellular automata.Free software: MIT licenseDocumentation:https://autocut3d.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-05-27)First release on PyPI.
Package: autocvd
autocvd, a tool for setting CUDA_VISIBLE_DEVICES based on GPU utilizationOn a system with multiple NVIDIA GPUs,autocvdeliminates the need for manually specifying theCUDA_VISIBLE_DEVICESenvironment variable. This comes in especially handy on systems with multiple users, like ashared GPU server. It isdependency-freeand requiresno code changes.To execute a command on a single free GPU, run$eval$(autocvd)<command>This will select a free GPU (or wait if none is available) and run the command with the appropriate environment variables set.For ease of use you might want to define an alias in your.bashrc, e.g., to run a Python script on a free GPU:$aliasgpupython="eval$(autocvd)python"Examples# run command on two free GPUs$eval$(autocvd-n2)<command># run command on least-used GPU (i.e., do not wait if no GPU is free)$eval$(autocvd-l)<command># exclude certain GPUs$eval$(autocvd-x02)<command># if no free GPU is available immediately, wait for 60 seconds only$eval$(autocvd-t60)<command># export environment variables into the current shell$.<(autocvd-e)# alternatively: source <(autocvd -e)Requirementsautocvdusesnvidia-smito query GPU utilization.
Make sure that it is installed and callable.InstallationpipinstallautocvdUsageusage: autocvd [-h] [-n NUM_GPUS] [-l] [-t TIMEOUT] [-i INTERVAL] [-e] [-o] [-q]

A tool for setting CUDA_VISIBLE_DEVICES based on utilization. Basic usage: eval $(autocvd) <command>

optional arguments:
  -h, --help            show this help message and exit
  -n NUM_GPUS, --num-gpus NUM_GPUS
                        Number of required GPUs. Defaults to 1.
  -l, --least-used      Select least-used GPUs instead of waiting for free GPUs. Defaults to False.
  -x EXCLUDE [EXCLUDE ...], --exclude EXCLUDE [EXCLUDE ...]
                        One or multiple GPUs (separated by space) to be excluded. Defaults to no GPU being excluded.
  -t TIMEOUT, --timeout TIMEOUT
                        Timeout for waiting in seconds. Defaults to no timeout.
  -i INTERVAL, --interval INTERVAL
                        Interval to query GPU status in seconds. Defaults to 30.
  -e, --export          Add 'export' statements such that environment can be sourced.
  -o, --id-only         Return comma-separated GPU IDs only instead of environment variable assignment.
  -q, --quiet           Do not print any messages. Defaults to False.autocvdcan also be used to set the environment variables from a Python script itself:fromautocvdimportautocvdautocvd()# code accessing GPUsNote that some packages readCUDA_VISIBLE_DEVICESwhen being imported, which makes it necessary to callautocvdbeforeimporting it.NotesBesides settingCUDA_VISIBLE_DEVICES,autocvdalso setsCUDA_DEVICE_ORDER=PCI_BUS_ID. This is required to ensure that the ordering of CUDA devices is consistent.Related ProjectscuthonsetGPU