Saravanan666/pypi-org huggingface dataset:
Package: autobuild
AutobuildAutobuildis a framework for building packages and for managing the
dependencies of a package on other packages. It provides a common
interface to configuring and building any package, but it is not a
build system like make or cmake. You will still need platform-specific
make, cmake, or project files to configure and build your
library. Autobuild will, however, allow you invoke these commands and
package the product with a common interface.Important: Linden Lab Autobuild is not the same as or derived from GNU
Autobuild, but they are similar enough to cause confusion.For more information, seeAutobuild's wiki page.Environment variablesNameDefaultDescriptionAUTOBUILD_ADDRSIZE32Target address sizeAUTOBUILD_BUILD_ID-Build identifierAUTOBUILD_CONFIGURATION-Target build configurationAUTOBUILD_CONFIG_FILEautobuild.xmlAutobuild configuration filenameAUTOBUILD_CPU_COUNT-Build system cpu core countAUTOBUILD_GITHUB_TOKEN-GitHub HTTP authorization token to use during package downloadAUTOBUILD_GITLAB_TOKEN-GitLab HTTP authorization token to use during package downloadAUTOBUILD_INSTALLABLE_CACHE-Location of local download cacheAUTOBUILD_LOGLEVELWARNINGLog levelAUTOBUILD_PLATFORM-Target platformAUTOBUILD_SCM_SEARCHtrueWhether to search for .git in parent directories if using SCM version discoveryAUTOBUILD_VARIABLES_FILE-.env file to loadAUTOBUILD_VCS_BRANCHgit branchautobuild-package.xml VCS info: branch name.AUTOBUILD_VCS_INFOfalseWhether to include version control information in autobuild-package.xmlAUTOBUILD_VCS_REVISIONgit commitautobuild-package.xml VCS commit reference to include in autobuild-package.xml. Defaults to current git commit sha.AUTOBUILD_VCS_URLgit remote urlautobuild-package.xml VCS info: repository URLAUTOBUILD_VSVER-Target Visual Studio version to use on windows
Package: autobuilder
# autobuilderA simple auto builder to build a group of AI assistants for task execution. It is built on [AutoGen framework](https://microsoft.github.io/autogen/docs/Getting-Started/), particularlyhttps://github.com/microsoft/autogen/blob/main/autogen/agentchat/contrib/agent_builder.pyIt is a simplified version of the “autobuilder” tool, integrated in our [LetMeDoIt AI project](https://github.com/eliranwong/letmedoit)# Install> pip install autobuilder# UsageImport as a module` from autobuilder import AutoGenBuilderAutoGenBuilder().getResponse("writea dummpy PySide6 app") `CLI options:> autobuilder> autobuilder -h> autobuilder “write a dummpy PySide6 app” # specify a task> autobuilder -c “saved_building_config.json” # load saved building config file> autobuilder -a 5 # specify the maximum number of agents> autobuilder -r 12 # specify the maximum round of group chat> autobuilder -o true # enable OpenAI Assistant API> autobuilder -o false # disable OpenAI Assistant API
Package: auto-build-minecraft
マイクラサーバー自動ビルドツールバージョン情報以下のバージョンで実行可能です。Python 3.7.3ansible [core 2.14.4]インストール以下のコマンドでインストールできます。pip install auto-build-minecraftセットアップansible 実行環境GCP 用の Ansible プラグインをインストールします。pip install google-auth google-auth-oauthlib google-auth-httplib2 google-cloud-sdkAnsible のコレクションをインストールします。ansible-galaxy collection install google.cloudGCP の認証GCP のサービスアカウントを作成し、サービスアカウントの認証ファイルをコマンド実行ディレクトリに入れます。設定ファイルコマンド実行ディレクトリにmsab.ymlファイルを作成し、以下のパラメータを記述してください。project_name: examplehostname: survival-mapuser_name: nerianighthawkzone: asia-northeast1-b実行以下で実行できます。msab {exec_type}コマンド実行時のexec_typeには以下を参考に指定してください。createの場合、サーバーの構築とインスタンスの作成を行うstopの場合、サーバーとインスタンスの停止を行うstartの場合、サーバーとインスタンスの起動を行うdeleteの場合、サーバーとインスタンスの削除を行うdownloadの場合、サーバーからワールドデータのダウンロードを行う開発者向け以下のコマンドで、ローカルのソースコードを実行コマンドとして登録できます。pip install -e .
Package: auto-bump-version
bump-versionAutomatically bump the version in setup.py, optionally creating a commit and tag.Installationpipinstallbump-versionUsageusage:bump-version[-h][--commit][--tag]{major,minor,patch}positionalarguments:{major,minor,patch}Thetypeofversionbump

options:-h,--helpshowthishelpmessageandexit--commitCreateacommit--tagCreateatag
Package: autoBWF
The purpose ofautoBWFis to provide an alternative GUI for embedding internal metadata in WAVE audio files using theBroadcast Wavestandard, FADGIBWFMetaEdit, andXMP.Unlike the existing BWFMetaEdit GUI, autoBWF is extremely opinionated and will automatically generate metadata content based on file naming conventions, system metadata, and pre-configured repository defaults. In addition, it can copy metadata fields from a template file to avoid having to enter the same information multiple times for several master or derivative files of the same physical instantiation.Also included are two command line programs to simplify the creation of derivative files.autolame.pyis a wrapper for thelameMP3 encoder that automatically transfers Wave internal metadata to appropriate ID3v2 tags.autospliceis a wrapper that generatessoxcommands from an EDL-like text file to splice audio from multiple input files, while also providing means to do basic fade in/out and audio level compression.For full installation, usage, and configuration information, please read the docs onreadthedocs.org.History3.6 (2022-08-14)Added the ability to export constant bitrate MP3 via autolame and the GUIBug fixes to prevent GUI crashes in corner cases3.5.3 (2021-02-09)Fix bug that caused GUI crash when copying text.3.5.2 (2020-04-15)Minor fixes and updates, includingadding Creator field to export utilitiesadditional code comments and minor bug fixes3.5.1 (2020-03-03)Documentation updates3.5.0 (2020-03-03)Add XML export capability to the GUIWarn user about unsaved dataClear GUI and reload file after saveAdd XMP Creator fieldbwf2csv can now validate MD5 of data chunkOther minor improvements and bug fixes3.4.0 (2019-12-14)Add capability to toggle between existing, tempate, and edited metadataAdd IARLAdd additional XMP MD fieldsAdd bwf2pbcore and bwf2csv tools3.3.1 (2019-07-22)Add "--config" argument to autoBWFCreate expanded documentation in Sphinx and host on readthedocs3.3.0 (2019-06-24)Add "quit" option to completion message boxAdd progress indicator screenChangeautolameusage to allow for multiple inputs and globbing3.2.1 (2019-06-22)Fixed bug that was resulting in the overwriting of metadata in template files (and other issues).3.2.0 (2019-05-14)Major rearrangment for packaging purposes.First release on PyPI.
Package: autocalc
A framework to keep track of dependencies in nonlinear workflows.Source codeAboutCreate event-driven apps in Python with ease, without writing callback functionsSet up the functional relationship between your public (visible to user) as well as internal (hidden) variables, and
let autocalc take care of keeping them in sync.ExampleIn this example we will implement a quadratic equation solver. User can enter thea,bandcparameters of thea*x*x + b*x +c = 0quadratic equation and we will calculate the two solutions:x1andx2by the following formulas:x1 = (-b-D)/(2a)x2 = (-b+D)/(2a)where D is defined asD=sqrt(b*b - 4*a*c)In this example we assume a Jupyter notebook environment and the use of ipywidgets. The library was designed with this setup in mind, but the core functionality is independent of any interactive environment.First we declare and display our input variablesimportipywidgetsaswidgetsfromautocalc.autocalcimportVarimportmatha=Var('a',initial_value=1,widget=widgets.FloatText())b=Var('b',initial_value=-3,widget=widgets.FloatText())c=Var('c',initial_value=1,widget=widgets.FloatText())display(a);display(b);display(c)Then we implement the code which calculates the solutiondefDfun(a,b,c):try:returnmath.sqrt(b*b-4*a*c)exceptValueError:returnmath.nandefx1fun(a,b,D):return(-b-D)/2/adefx2fun(a,b,D):return(-b+D)/2/aWe are now ready to define and display our internal variable:D=sqrt(b*b - 4*a*c)and output variables:x1andx2D=Var('D',fun=Dfun,inputs=[a,b,c])x1=Var('X1',fun=x1fun,inputs=[a,b,D],widget=widgets.FloatText(),read_only=True)x2=Var('X2',fun=x2fun,inputs=[a,b,D],widget=widgets.FloatText(),read_only=True)display(x1)display(x2)That’s it. With just a few lines we set up the dependency graph from our input variables to our output ones. Any time the user upgrades any of the input variables, the output will be updated automatically.FeaturesIpywidgets integration + visual framework independent mode of operation.Read-only variablesLightweight, minimal library. Learn the basics in 10 minutes.“Lazy” variables, which only get invalidated when their input(s) change, but are not actually recalculated until explicitly requested. Useful for functions which take a long time to evaluate.
Package: autocalver
autocalverTired of
"bumping versions"?
Sick of commits,
pull requests,
tags,
and a lot of manual work just to release
the code that is already in the main branch?Theautocalverpackage is here to automatically generate
"calendar versions",
and chew gum,
and it's all out of gum.It automatically produces a calver based on a commit log.
The version depends on the
time of thecommit,
not the time of the build.
The version format is<year>.<month>.<day>.<seconds from beginning of day>.
The time is always converted to UTC first.ConfigurationThe right way to make sureautocalveris installed is to require it inpyproject.toml:[build-system]requires=["setuptools>=45","wheel","autocalver"]Note that,
by default,autocalverwillnotrun any version-control commands.
Before usingautocalver,
run a command to pull a prefix of the commit log,
and save it to a file.
For example,
withgit,
you can usegitlog-n1--date=iso>git-log-headOne advantage of separating the stages like that is that it is possible
to have the package builder itself running in an environent that does not
have access to the version control metadata,
only the source files and the prefix of the commit log.The tool overrides the version in thesetup.cfg,
if any.
Configuration is done viapyproject.toml.
For example,
a configuration appropriate to
pipeline CI might be:[tool.autocalver]use=truelog="git-log-head"is_main_var="BUILD_BRANCH_REF"is_main_match=".*/main$"Note that if you use the[project]entry inpyproject.toml,
it must:Not haveversionExplicitly declaredynamic = ["version"]Building packagesBased on the given environment variable
and its value
the version is produced as development,
rc,
or a full-release.
This allows checking against a release-system's
environment variables for the branch,
pull request,
or workflow name.With this configuration,
the following versions will be produced
(asssuming the time of the latest commit is
October 10, 2021, 8:39:44 in Pacific time).Local build:$python-mbuild-n--wheel2>&1|tail-1Successfully built fake_package-2021.10.10.56384.dev1-py3-none-any.whlNote that the version has adev1in its name.Simulate tagged build:$GITHUB_REF=refs/tags/test-fix-PROJ-121python-mbuild-n--wheel2>&1|tail-1Successfully built fake_package-2021.10.10.56384rc1-py3-none-any.whlThis version has anrc1in the name,
marking it as a pre-release.
This allows pushing tags to produce packages
and test fixes.
However,
since tag pushes are not reviewed,
these do not produce releases.Simulate merge to main:$GITHUB_REF=refs/heads/mainpython-mbuild-n--wheel2>&1|tail-1Successfully built fake_package-2021.10.10.56384-py3-none-any.whlNote that withoutuse = true,
the tool will not replace the version.
Ifuse = trueis there,
missing any of the other variables
will cause anerrorduring the build,
as a non-existing build
is better than a broken when.Automatically fetching the latest commitDefining the optional fieldlog_commandwill run the command
if the file is unavailable
at package build time.
It will create the file
for next time.
Note that if the file is
"out of date",
it needs to be manually removed.
Package: auto-caption
Auto-caption.Install.Install ffmpeg first.sudoaptinstallffmpegInstall through pypi.pip3installauto-captionUsage.usage:auto-caption[-h][--formatFORMAT][--outputOUTPUT]video

Automaticcaptioningformovies.

positionalarguments:videoThepathofinputvideo

optionalarguments:-h,--helpshowthishelpmessageandexit--formatFORMATOutputformat(vtt/srt,default:vtt)--outputOUTPUTThepathtowritesubtitlefileSample.Clickhereto see sample captions generated by this program.
Package: autocat
AutoCatAutoCat is a suite of python tools forsequential learning for materials applicationsandautomating structure generation for DFT catalysis studies.Documentation for the package can be foundhere.Development of this package stems fromACED, as part of the
ARPA-E DIFFERENTIATE program.InstallationThere are two options for installation, either viapipor from the repo directly.pip(recommended)If you are planning on strictly using AutoCat rather than contributing to development,
we recommend usingpipwithin a virtual environment (e.g.conda). This can be done as follows:pip install autocatGithub (for developers)Alternatively, if you would like to contribute to the development of this software,
AutoCat can be installed via a clone from Github. First, you'll need to clone the
github repo to your local machine (or wherever you'd like to use AutoCat) usinggit clone. Once the repo has been cloned, you can install AutoCat as an editable
package by changing into the created directory (the one withsetup.py) and installing
via:pip install -e .ContributingContributions through issues, feature requests, and pull requests are welcome.
Guidelines are providedhere.AcknowledgementsThe code presented herein was funded by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DE-AR0001211 and in part by the National Science Foundation, under Award Number CBET-1554273. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.
Package: autochat
AutoChatAutoChat is a lightweight interface to the ChatGPT API, to simplify the process of creating conversational agents.ChatGPT Class: Conversation wrapper to store instruction, context and messages histories.Message Class: Message wrapper to handle format/parsing automatically.Function Calls: Capability to handle function calls within the conversation, allowing complex interactions and responses.Template System: A straightforward text-based template system for defining the behavior of the chatbot, making it easy to customize its responses and actions.InstallationTo install the package, you can use pip:pipinstallautochatPlease note that this package requires Python 3.6 or later.Simple Example>fromautochatimportChatGPT>chat=ChatGPT(instruction="You are a parot")>chat.ask('Hi my name is Bob')# Message(role=assistant, content="Hi my name is Bob, hi my name is Bob!")>chat.ask('Can you tell me my name?')# Message(role=assistant, content="Your name is Bob, your name is Bob!")Template SystemWe provide a simple template system for defining the behavior of the chatbot, using markdown-like syntax.## system
You are a parrot

## user
Hi my name is Bob

## assistant
Hi my name is Bob, hi my name is Bob!

## user
Can you tell me my name?

## assistant
Your name is Bob, your name is Bob!You can then load the template file using thefrom_templatemethod:parrotGPT=ChatGPT.from_template("./parrot_template.txt")The template system also supports function calls. Check out theexamples/classify.pyfor a complete example.Function Calls HandlingThe library supports function calls, handling the back-and-forth between the system and the assistant.fromautochatimportChatGPT,Messageimportjsondeflabel_item(category:str,from_response:Message):# TODO: Implement functionraiseNotImplementedError()withopen("./examples/function_label.json")asf:FUNCTION_LABEL_ITEM=json.load(f)classifierGPT=ChatGPT.from_template("./examples/classify_template.txt")classifierGPT.add_function(label_item,FUNCTION_LABEL_ITEM)text="The new iPhone is out"formessageinclassifierGPT.run_conversation(text):print(message.to_markdown())# > ## assistant# > It's about \"Technology\" since it's about a new iPhone.# > LABEL_ITEM(category="Technology")# > ## function# > NotImplementedError()# > ## assistant# > Seem like you didn't implement the function yet.Environment VariablesTheOPENAI_MODELenvironment variable specifies the OpenAI model to use. If not set, it defaults to "gpt-4".exportOPENAI_MODEL="gpt-4"exportOPENAI_API_KEY=<your-key>SupportIf you encounter any issues or have questions, please file an issue on the GitHub project page.LicenseThis project is licensed under the terms of the MIT license.
Package: autocheck
Run Python unittests automatically. Re-run tests when source has
changed. Try to make intelligent decisions about which tests to run.ExampleIn project directory with unittests:$ autocheck discover -vAccepts the same options aspython-munittest.Installation$ pip install autocheckOptionally, for Growl support:$ pip install gntpInstallwatchdogkernel support according tohttps://pythonhosted.org/watchdog/installation.html.Django supportTell django to use our test runner, insettings:TEST_RUNNER = 'autocheck.contrib.django.TestSuiteRunner'Or on the command line:./manage.py test --testrunner=autocheck.contrib.django.TestSuiteRunnerRun tests automatically whenever source has changed:$ autocheckautochecktries to figure out if it runs in a django project
(./manage.pyexists, containsDJANGO_SETTINGS_MODULE,djangois importable). Additionally, if a filetest_settings.pyexists,DJANGO_SETTINGS_MODULE=test_settingsis added to the environment.Behind the scenes, there are two test runners for django, selected
during import ofautocheck.contrib.django.TestSuiteRunner:autocheck.contrib.django.discoveryrunner.TestSuiteRunnerfor
recent versions of django (>=1.6)autocheck.contrib.django.testsuiterunner.TestSuiteRunnerfor
older versions (<1.6)The latter is not compatible with the old./manage.py testcommand,
instead it tries to reproduce the interface ofpython-munittest.The other one is a thin wrapper around django’sDiscoverRunner,
adding a few command line switches for our customTestRunner.StatisticsDump the test database with:$ autocheck --statsOr for flat output with:$ autocheck --stats-flatTestsRun in project directory:$ python setup.py testRun tests using our own test runner:$ pip install -e .
$ pip install -r requirements/test.txt
$ python -m autocheck discover -v --once -s ./vows -t .Tested against Python-2.6, 2.7, 3.3, 3.4, PyPy 2 and 3.TODOmakeautocheck.autorunnerfile pattern configurableconfig file(s)documentation for tags and tag expressionsdocumentation for autocheck command line flags--helpfor autocheck commandLicenseThis is free and unencumbered software released into the public domain.seeUNLICENSE
Package: auto-check
auto-checkis a tool used to check in & out in the remote server automaticly. Set a crontab task that run this script in the remote linux system.The check in & out system was a little bit funny, because this api(url)’s network package was captured while using my iPhone App to perform check in & out actions, and I check in & out successfully via these api with cookies, etc.Installationpip3 install auto-checkUsageThe following setting will perform an action thatcio.pywas executed at 8:30 and 19:30 on every workday.30 8,19 * * * auto-checkFAQWhy performs login aciton before check in & out?In order to avoid the cookies being out of date, though that case didn’t appear. But when I open the iPhone App, the lonin action was performed, and the latest cookie was received by the App.
Package: autochomsky
#Auto-Chomsky phrase generatorChomsky sentence generator.Chomsky is an aid to writing linguistic papers in the style
of the great master.  It is based on selected phrases taken
from actual books and articles written by Noam Chomsky.
Upon request, it assembles the phrases in the elegant
stylistic patterns that Chomsky is noted for.To generate sentences of linguistic wisdom in a paragraphFor 1 sentence wrapped at 72 characters:> chomsky()For n sentences wrapped at 72 characters:> chomsky(n)For n sentence wrapped at x characters:> chomsky(n, x)The phrase database was converted directly from someone else’s Lisp program:http://www-personal.umich.edu/~jlawler/foggy.lspCreated by Raymond Hettinger, 2005http://code.activestate.com/recipes/440546-chomsky-random-text-generator/Modified by Greg Fitch, OpenStax, 2020https://github.com/gregfitch
Package: autochord
autochordAutomatic Chord Recognition toolsAboutautochordis:✔ aPython libraryfor automatic chord recognition (using TensorFlow)✔ aJavascript appfor visualization of chord transcriptions:Library UsageTo install library, run:$ pip install autochordautochordprovides a very simple API for performing chord recognition:import autochord
autochord.recognize('audio.wav', lab_fn='chords.lab')
# This gives out a list of tuples in the format:
#  (chord start, chord end, chord name)
# e.g.
# [(0.0, 5.944308390022676, 'D:maj'),
#  (5.944308390022676, 7.476825396825397, 'C:maj'),
#  (7.476825396825397, 18.250884353741498, 'D:maj'),
#  (18.250884353741498, 19.736961451247165, 'C:maj')
#  ...
#  (160.49632653061224, 162.30748299319728, 'N')]Under the hoodautochord.recognize()runs the NNLS-Chroma VAMP plugin to extract chroma features from the audio, and feeds it to a Bi-LSTM-CRF model in TensorFlow to recognize the chords. Currently, the model can recognize 25 chord classes: the 12 major triads, 12 minor triads, and no-chord ('N').OPTIONALLY, you may dump the chords in a.labfile by using thelab_fnparameter. The output file follows the MIREX chord label format.Upon importautochordtakes care of setting up the VAMP plugin and downloading the pre-trained chord recognition model.The measured test accuracy of the TensorFlow model is 67.33%. That may be enough for some songs, but we can explore in the future how to further improve this.App UsageThe app is pretty straightforward: you need to load a song, then you can upload a LAB file to visualize its chord labels. You may use theautochordPython library for generating this file. Optionally, you may load another LAB file for comparison (e.g. ground-truth labels, LAB file from another model's prediction).Future ImprovementsIntegrate everything into a full chord recognition app! For that we need to:convert VAMP plugin to JS modulemodel conversion to TensorFlow.js (as of writing, some CRF operations are not supported by TFJS yet)converting all other Python functions to JS equivalentExperimenting with other approaches to improve chord recognition accuracy
Package: autoclasstoc
It’s surprisingly difficult to document large Python classes in a way that’s
easy for users to navigate.  Most projects use theautodocSphinx plugin,
which simply puts the complete documentation for each class member one after
another.  While this does fully document the class, it doesn’t give the user a
quick way to see everything the class can do.  This makes classes of even
moderate complexity difficult to navigate.  It also encourages projects to be
stingy about which class members to include in the documentation (e.g.
excluding special methods, inherited methods, private methods, and/or
undocumented methods), to the further detriment of the user.What’s needed is for each class to have a succinct table of contents (TOC)
that:Is organized into sections that will be meaningful to the user.  Different
projects and classes may call for different sections, e.g. public/private
methods, methods that share a decorator, methods with a common prefix, etc.Includes every method of the class (so that the documentation is complete),
while still making it easy for the user to get a sense for what the class
does and find what they’re looking for.Collapses inherited methods.  Complex classes in particular can inherit a lot
of methods from their parent classes, and while these methods should be
present in the TOC (since they’re part of the class), collapsing them makes
it easier for the user to grok the functionality provided by the class
itself.autoclasstocprovides a new Restructured Text directive that is all of
these things.  It also works well withautodocandautogen, and should
be easy to incorporate into any existing project.See thecomplete documentationfor more information (including examples).