Saravanan666/pypi-org huggingface dataset:
Package: autoreject
autorejectThis is a library to automatically reject bad trials and repair bad sensors in magneto-/electroencephalography (M/EEG) data.The documentation can be found under the following links:for thestable releasefor thelatest (development) versionInstallationWe recommend theAnaconda Python distributionand aPython version >= 3.8.
To obtain the stable release ofautoreject, you can usepip:pip install -U autorejectOrconda:conda install -c conda-forge autorejectIf you want the latest (development) version ofautoreject, use:pip install https://api.github.com/repos/autoreject/autoreject/zipball/masterIf you do not have admin privileges on the computer, use the--userflag
withpip.To check if everything worked fine, you can do:python -c 'import autoreject'and it should not give any error messages.Below, we list the dependencies forautoreject.
All required dependencies are installed automatically when you installautoreject.mne(>=1.0)numpy(>=1.20.2)scipy(>=1.6.3)scikit-learn(>=0.24.2)joblibmatplotlib(>=3.4.0)Optional dependencies are:openneuro-py(>= 2021.10.1, for fetching data fromOpenNeuro.org)QuickstartThe easiest way to get started is to copy the following three lines of code
in your script:>>>fromautorejectimportAutoReject>>>ar=AutoReject()>>>epochs_clean=ar.fit_transform(epochs)# doctest: +SKIPThis will automatically clean anepochsobject read in using MNE-Python. To get the
rejection dictionary, simply do:>>>fromautorejectimportget_rejection_threshold>>>reject=get_rejection_threshold(epochs)# doctest: +SKIPWe also implement RANSAC from thePREP pipeline(seePyPREPfor a full implementation of the PREP pipeline).
The API is the same:>>>fromautorejectimportRansac>>>rsc=Ransac()>>>epochs_clean=rsc.fit_transform(epochs)# doctest: +SKIPFor more details check out the example toautomatically detect and repair bad epochs.Bug reportsPlease use theGitHub issue trackerto report bugs.Cite[1] Mainak Jas, Denis Engemann, Federico Raimondo, Yousra Bekhti, and Alexandre Gramfort, “Automated rejection and repair of bad trials in MEG/EEG.”
In 6th International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2016.[2] Mainak Jas, Denis Engemann, Yousra Bekhti, Federico Raimondo, and Alexandre Gramfort. 2017.
“Autoreject: Automated artifact rejection for MEG and EEG data”.
NeuroImage, 159, 417-429.
Package: autorelease
AutoreleaseRelease management for GitHub and continuous integration, based on branches.
The basic philosophy is to maintain development branches (which always have
development versions of the code) and release branches (which always have
release versions of the code). The workflow for a release is therefore:Update the version for release and make a PR to a stable branch; the top
post will be the release notes.Merge the PR.That's it. Autorelease handles the rest.When you make the PR to a stable branch, Autorelease will deploy the package to
testpypi, and re-download it and test it again. This ensures that you don't
publish broken packages. After you merge to the stable branch, Autorelease will
cut a new release on GitHub, and then publish the release on PyPI.Tools included:Travis config imports and scripts to automatically test-deploy on testpypi,
then cut a GitHub release, then deploy to PyPI.Vendor-able GitHub Actions workflows for test-deploy, GitHub release, and
PyPI deploy.Vendor-ableversion.pythat gives one true location for version
(setup.cfg) while also enabling developer installs to give full and correct
version information.Vendor-ablesetup.pythat keeps all user-required information in thesetup.cfg.Script to draft release notes based on labels on merged PRs.If you're a Python developer who uses Travis and GitHub, Autorelease handles
everything related to releasing to PyPI.
Package: auto-reloader
Welcome to auto-reloader 👋Automatic reloading library for JupyterInstallpipinstallauto-reloaderUsageimportmoduleas_modulefromauto_reloaderimportAutoReloadermodule=AutoReloader(_module)instance=module.Class()The module is reloaded when an instance is created withinstance = module.Class().Besides this, the module is reloaded every time you access themoduleattribute.Run testspoetryrunpytesttestsAuthor👤moisutsuTwitter:@moisutsuGithub:@moisutsuShow your supportGive a ⭐️ if this project helped you!📝 LicenseCopyright © 2020moisutsu.This project isMITlicensed.This README was generated with ❤️ byreadme-md-generator
Package: auto-remote-sync
Auto rsyncCommand to automate execution of various rsync commands based on profiles
defined on a YAML configuration file.Instead of doing:rsync-avySH--delete--backup--backup-dir=../deleted/$timestamp/"/media/Media/Photos""user@host.com:/media/backup/filesets/$hostname.photos"Just do:autorsync-pphotosInstead of doing:rsync-avySH--delete--backup--backup-dir=../deleted/$timestamp/"/media/Media/Photos""user@host.com:/media/backup/filesets/$hostname.photos"rsync-avySH--delete--backup--backup-dir=../deleted/$timestamp/"/media/Media/Books""user@host.com:/media/backup/filesets/$hostname.books"rsync-avySH--delete--backup--backup-dir=../deleted/$timestamp/"/media/Media/Music""user@host.com:/media/backup/filesets/$hostname.music"Just do:autorsyncOr, in more general terms, instead of doing long rsync commands for your
everyday backups, or putting them in adhoc scripts, write the clear~/autorsync.yamlfile and letautorsyncto the work for you.Installationpip3installauto-remote-sync--user(Sorry for the long package name, ideally this would be simplyautorsync,
but nameautosyncwas already taken and PyPi would not allow similar names).CheckPyPiandGitHubfor autorsync.UsageOrganize Profiles in~/autorsync.yamlHere is an example with some defaults and a few profiles:DEFAULTS:source_part1:'{{home}}/Media'target_part1:user@remote.host.com:/media/backup/filesetsdelete:Truebackup:Truebackup_dir:../deleted/{{time.strftime('%Y.%m.%d-%H.%M.%S')}}/background:Falseprofiles:-name:bookssource_part2:Books/target_part2:'{{hostname}}.books/files'background:True-name:nextcloud.datasource:/var/lib/nextcloud/datatarget_part2:'{{hostname}}.nextcloud_files'extra:--copy-links --no-atimes --itemize-changesNotes about this configurationAll profiles inherit parameters fromDEFAULTS. If parameter isn’t set in
the profile, the value defined inDEFAULTSwill be used.For each profile, the Source is defined bysourceparameter, or, if not
defined, bysource_part1/source_part2Target follows same logic:targetortarget_part1/target_part2deletemakes rsync delete files in target that are absent in sourcebackupandbackup_dirmakes rsync save backups on target of deleted or
modified files on source. Value onbackup_diris a path relative to target
folderextralets you add extra rsync switches and can be used in theDEFAULTSsection (to affect all profiles) or just into a specific profile.You can use Jinja logic in path parts, surrounded by{{}}. Currently these
are the available variables:time, a Pythondatetime.datetimeobject which includes local timezonehostname, such as “rocket”Hostname, such as “rocket.mydomain.com” (FQDN)username, UNIX user name as “joanbaez”home, user’s home folder as “/home/joanbaez”userid, user ID as “504”gecos, user long name as “Joan Baez”Example usageShow all profiles:autorsync-lRun rsync for all profiles:autorsyncRun rsync only for profilebooksautorsync-pbooksSimulate rsync only for profilebooks(force rsync’s--dry-run)autorsync-n-pbooksorautorsync--dry-run-pbooksRun rsync for 2 profiles from a non-default configuration file:autorsync-c/etc/autorsync.yaml-p"books, photos"
Package: autorepo
AutoRepo (v0.1.7)AutoRepo is a simple, cli-based tool for creating GitHub repositories, without leaving your terminal!InstallationRun the following command to install autorepo:$ pip install autorepoOr$ pipx install autorepoUsageAfter the installation is complete, you can run the following command to print the help message:$ repo --helpUsing any command (displayed in the help message) requires you to first be logged in to your GitHub account using a Personal Access Token (this is a one-time thing). You can generate a PAT by following the instructionshere:$ repo loginAfter logging in, you can create a new repository by running the following command:$ repo create [OPTIONS] NAMETo convert an existing project directory into a GitHub repository, run the following command (the-eflag is used to specify that the directory contains an existing project):$ cd <project_directory>
$ repo create -e [OPTIONS] NAMETo logout of your GitHub account, run the following command:$ repo logout
Package: autoreport
RequirementsTested with Django 1.1.1.
Requires relatorio.How to use itInstall it by adding ‘autoreport’ to INSTALLED_APPS in your settings,
(it requires running syncdb again for new models to be installed).Then, add a line like this into your main URLConf:(r’^autoreport/’, include(‘autoreport.urls’)),You should overwrite templates from autoreport’s templates folder
in order to match your project.
Package: autoreporter
autoreporterAutoreporter generates beautiful reports laid out using a customizable PowerPoint template. It interfaces with Matplotlib and Plotly for producing figures, and it does text variable substitution as well.InstallationExampleAutoreporter fills in the templateto create the report.To create this, this function is called:fromautoreporterimportbuilderbuilder.builder("report.pptx","test.pptx",fig_dict,var_dict={"a":"123"})PowerPoint shapes with text of form "!(key)" is replaced with the matplotlib figure,fig_dict[key].Text of the form "$[key]" is replaced with a variable,var_dict[key].Any other shape, image or text is kept the same.
Package: auto-reports-sql
SQL AutomationThis is a simple tool to run a set of sql queries and generate a HTML report of the resultsHow to use the toolInstall the tool using pippipinstallauto-reports-sqlCreate a json file with the following schema{"type":"array","items":{"type":"object","required":["name","query"],"properties":{"name":{"type":"string","pattern":"^.*$"},"query":{"type":"array","items":{"type":"string","pattern":"^.*$"}}}}}Example[{"name":"First 10 data","query":["select * from Person limit 10","select * from Person order by name desc limit 10"]}]Run the program with the following commandUsage: auto-reports-sql execute [OPTIONS] QUERIES

Options:
  -t, --threads INTEGER           No. of threads used for computation
                                  [default: 10]
  -rt, --report-title TEXT        Title of the generated report.  [default: Report Title]
  -d, --db [sqlite|mysql|postgres]
                                  The database which is used.  [required]
  -dp, --db-path PATH             Sqlite database path. Only required if the
                                  database is set to 'sqlite'
  -h, --host TEXT                 host of the database. Only required if the
                                  database is not set to 'sqlite'
  -u, --username TEXT             username to access the database. Only
                                  required if the database is not set to
                                  'sqlite'
  -p, --password TEXT             password of the database. Only required if
                                  the database is not set to 'sqlite'
  -n, --db-name TEXT              name of the database. Only required if the
                                  database is not set to 'sqlite'
  --config FILE                   Read configuration from FILE.
  --help                          Show this message and exit.Report ExamplesPossible UpdatesErrors in the queries are not taken into account
Package: autorequests
AutoRequestsGenerate Python code to recreate a request from your browser.📺 Demo💼 Example Use CasesCreating a foundation for an API wrapperRecreating a request outside the browserTesting what cookies or headers are required for a server to understand your request✂️ How to CopyInspect ElementGo toNetworktabFind web requestRight-ClickCopyChoose one of the following:PowershellNode.js fetch📦 Installationinstall the package with pip$ pip install autorequestsor download the latest development build from GitHub$ pip install -U git+https://github.com/Hexiro/autorequests🖥️ Command Line$autorequests--helpMeta Options--file  -f            Optional file to read input from.--copy  -c            Copy the output to the clipboardGeneration options-sync/--async  -s/-a  Generate synchronous or asynchronous code.--httpx        -h     Use httpx library to make requests.--no-headers   -nh    Don't include headers in the generated output.--no-cookies   -nc    Don't include cookies in the generated output.🐞 ContributingseeCONTRIBUTING.md
Package: autoresearcher
🤖🧪 AutoResearcher⚡ Automating scientific workflows with AI ⚡What is AutoResearcher?AutoResearcher is an open-source Python package that combines AI models and external knowledge sources to automate scientific workflows. It is designed to help researchers and scientists to speed up their research process and to make it more efficient.The project is a very early prototype and is still under development. Currently, it is limited to conducting literature reviews. The vision, however, is to create a tool that can conduct actual scientific discovery on autopilot.If this vision excites you, please consider contributing to the project. You can start by joining theDiscord serverand discussing your ideas with the community.InstallationInstall the package using pip:pipinstallautoresearcherSetting Environment VariablesBefore using the package, you need to set the following environment variables:OPENAI_API_KEY: Your OpenAI API key for accessing the GPT-based AI models.EMAIL: An email address of your choice (used to identify your API requests for getting citations).You can set the environment variables in your operating system or in your Python script using theosmodule:importosos.environ["OPENAI_API_KEY"]="<your_openai_api_key>"os.environ["EMAIL"]="<your_email>"Replace <your_openai_api_key> and <your_email> with your actual API key and email address.UsageImport the literature_review function from the package:fromautoresearcherimportliterature_reviewSet your research question as a string:research_question="What is the best way to train a neural network?"Create a literature_review instance with your research question:researcher=literature_review(research_question)You can also pass an output file name as a .txt file:researcher=literature_review(research_question,output_file="my_literature_review.txt")Execute the researcher instance to fetch and analyze relevant papers:researcher()This will generate a literature review based on the research question.ContributingContributions are welcome! Please feel free to submit issues or create pull requests. Let's take upgrade science together! 🚀LicenseThis project is licensed under the MIT License. See the LICENSE file for details.Made with ☕ by@eimenhamedat
Package: autoresevaluator
AutoRes EvaluatorArchitectureHow to useInstallhttps://pypi.org/project/autoresevaluator/pipinstallautoresevaluatorfromautoresevaluatorimportAutoResEvaluatorSetting# Hyperparameter setting# Specify "type" and "args" for items to be searched in optuna.params={'lambda_l1':{'type':'log_float','args':[1e-8,10.0]},'lambda_l2':{'type':'log_float','args':[1e-8,10.0]},'num_leaves':{'type':'int','args':[2,256]},'feature_fraction':{'type':'float','args':[0.4,1.0]},'bagging_fraction':{'type':'float','args':[0.4,1.0]},'verbosity':-1}are=AutoResEvaluator(# task typetask_type='tabledata binary classification',# dataset namedataset_name='titanic',# model file pathmodel_path='/Users/tanakatouma/vscode/autores-evaluator/test/lightgbm_model.py',params=params,# Metrics you want to maximize/minimizevaluation_index='pr_auc')Executionare.exec()Outputresult.logFile to output the resultsmodel_error.logFile to write errors in model files
Package: auto-resizing-text-edit
AutoResizingTextEdit is a simple Qt widget based on QTextEdit. Its purpose is to automatically adjust editor height to match the text, with the help of a layout it’s placed in.It works with both Python 2 and Python 3 as well as PyQt 4 and PyQt 5[2]StatusThe project has not undergone extensive testing on multiple platorms yet and should be considered alpha-quality. It’s already feature-complete though and, due to its simplicity and emphasis on working with Qt widget system rather than subverting it, should be safe for general use.The code has only been tested on Arch Linux so far.Installationsetup.pyThe repository contains standard setup.py script (using setuptools). To install run:pythonsetup.pyinstall--root=/your/python/site-packages/path--optimize=1For a complete command reference seesetuptools documentation.PyPIIf you’re using pip you can install the package from PyPI repository:auto-resizing-text-edit on PyPIpipinstallauto-resizing-text-editYou can install the PyQt 4 versions that way as well:pipinstallgit+https://github.com/cameel/auto-resizing-text-edit.git@pyqt4-v0.1.0AURIf you’re using Arch Linux you can get a PKGBUILD from AUR:python-auto-resizing-text-edit in AURDependenciesPython >= 2.7PyQt 5 or PyQt 4[1][2][1]Note that PyQt is not listed explicitly in setup.py. That’s because it can’t be easily installed from PyPI and setup.py/pip fails trying to do so. Moreover, it does not provide the .egg-info which makes setuptools fail to detect it even if you have already installed it from a different source. For these reasons you’re expected to install PyQt yourself, possibly from your distribution’s software repository. If you’re installing in a virtualenv you can use –system-site-packages option or copy globally installed PyQt to it.[2](1,2)Only PyQt 5 version is provided as PyPI package but you can find versions ported to PyQt 4 under tags prefixed with pyqt4 (e.g. pyqt4-v0.1.0).UsageIt’s as simple as:fromauto_resizing_text_editimportAutoResizingTextEditeditor=AutoResizingTextEdit()The widget also provides a simple way to constrain its minimum width to a specific number of lines (provided that all the text in the editor uses the same font; if not, you’re on your own here):editor.setMinimumLines(3)That’s all there is to it. You put it in a layout and forget about it:fromPyQt5.QtWidgetsimportQWidget,QVBoxLayoutwidget=QWidget()editor.setParent(widget)layout=QVBoxLayout(widget)layout.addWidget(editor)layout.addStretch()widget.setLayout(layout)If you need more or just want to see the editor in action, there are a few simple but complete examples in examples/ directory.LicenseCopyright © Kamil ŚliwakReleased under theMIT License.
Package: autoresponse
This is a small bit of Python code that wraps various Request objects
and returns a corresponding “Response”. The responses, in this case,
come from saved files on disk rather than from actually loading the
web page.The intended use of this module is in automated testing of scraping
code.For now, it targets only “scrapy”. It’d be great to have a version of
this that targeted “python-requests” as well.Contributions are very welcome, and we use the bug tracker in the Github
project.Github URL:https://github.com/paulproteus/autoresponse
Package: auto-restart
安装 pip install auto_restart当检测到git内容发生变化后，自动重启部署，是冷部不是热部署。建议在开发环境开发好后，代码推到测试分支。建议安装screen, apt-get install screen，(也可以不安装这个screen，使用nohup启动脚本了那就需要)在screen会话里面 运行以下命令:auto_restart_tool -d /home/ydf/pycodes/auto_restart  -s  "python3  tests/test_git_change.py" -k tests/test_git_change.pyauto_restart_tool命令是自动生成的，可以直接使用这条命令。之后，只要当前分支的git内容有更新就会自动重启。python3 ./auto_restart/auto_restart_when_git_changed.py -d /home/ydf/pycodes/auto_restart  -s  "python3  tests/test_git_change.py" -k tests/test_git_change.py
Package: auto-retrieval-plugin
Auto ChatGPT Retrieval PluginEffortlessly create and deploy your own ChatGPT Retrieval Plugins withauto-retrieval-plugin, a powerful command-line tool that takes care of hosting and server setup for you!Quick StartInstallationEnsure you have Python 3.8 or later.Install the tool via pip:pipinstallauto-retrieval-pluginDeploymentRun the following command to deploy the plugin:auto-retrieval-plugindeploy--key<youropenaikey>Store the "Gateway (Http)" URL and the Bearer token provided in the output.╭────────────────────────🎉Flowisavailable!────────────────────────╮
││
│IDretrieval-plugin-<pluginid>│
│Gateway(Http)https://retrieval-plugin-<pluginid>.wolf.jina.ai│
│Dashboardhttps://dashboard.wolf.jina.ai/flow/<pluginid>│
││
╰───────────────────────────────────────────────────────────────────────╯
Bearertoken:<yourbearertoken>Data IndexingGather relevant text data files (PDF, TXT, DOCX, PPTX, or MD) in a directory.Index the data:auto-retrieval-pluginindex--datamy_filesOr, usedocarray (v0.21.0)for text data:fromdocarrayimportDocument,DocumentArraytexts=['Text 1','Text 2','Text 3']docs=DocumentArray([Document(text=text)fortextintexts])docs.save_binary('docs.bin')And then:auto-retrieval-pluginindex--datadocs.binIntegrationGo to OpenAI Plugins.Select "Develop your own plugin".Enter the "Gateway (Http)" URL and Bearer token from the deployment step.Advanced UsageConfigurationTo tailor the plugin to your needs, change the name and description during deployment:auto-retrieval-plugindeploy--key<youropenaikey>--name"Custom Name"--description"Custom description"If not specified, default values will be used.ArgumentDescriptionDefault ValuenameHuman-readable name, such as the full company nameRetrieval PlugindescriptionDescription better tailored to the model, such as token context length considerations or keyword usage for improved plugin promptingPlugin for searching through the user's documents (such as files, emails, and more) to find answers to questions and retrieve relevant information. Use it whenever a user asks something that might be found in their personal informationListing PluginsList your plugins and their status:auto-retrieval-pluginlistOutput:PluginID:ece735568f|Status:ServingDeleting PluginsDelete a plugin:auto-retrieval-plugindelete<pluginid>Indexing Specific PluginsIndex data for a specific plugin:auto-retrieval-pluginindex--datamy_files--id<plugin_id>If the plugin ID is not specified, the last created plugin will be indexed.
Package: autoretriever
# autoretrieverA simple data retriever chat, built on [AutoGen framework](https://microsoft.github.io/autogen/docs/Getting-Started/).It is a simplified version of the “autoretriever” tool, integrated in our [LetMeDoIt AI project](https://github.com/eliranwong/letmedoit)# Install> pip install autoretriever# Usage> autoretriever
Package: auto-retry-sshfs
IdeaAllows you to run sshfs in such a way that you don't have to worry about it crashing for whatever reason. Automatically will unmount and remount your sshfs drive if it crashes. Great for if you're using sshfs over a noisy connection or over kerebros or something.Usageauto-retry-sshfs --source-server user@server --source-path /path --target-path /path