Saravanan666/pypi-org huggingface dataset:
Package: dailylog
Project descriptionDailylog is a utility for recording daily logs on your own word.You can easily master its usage through this document.Why should I use dailylog?The goal of dailylog is to provide a simple way to create daily-log files. In the build-in moduleslogging,
which has to config the date and log format, besides, if you want to devide the logs by day, you will need to create a
TimedRotatingFileHandler.But in the dailylog, we provide a simply way to handle the log files, you can flexibly record what you want, avoiding to
take some unimportant information, such as trackback, warning from the django, and so on. Also, the module can
automatically make daily-log files.Installation$pip install dailylognowadays we only provide linux wheel, if you are using Windows platform, you can download the source file and run the following command:$python setup.py installUsing dailylogimport the module and create a filehandler:import dailylog
file_handler = dailylog.LogHandler()we provide three parameters for LogHandler:date_format: as named, default is "%Y-%m-%d--%H:%M:%S"log_dir: target folder to save the log files, default is "./logs/". if the folder does not exist, the system will create it automatically.print_symbol: whether to print the message on the terminal. Default isTruemake different logs:file_handler.debug(some message)
file_handler.info(some message)
file_handler.warning(some message)
file_handler.error(some message)show different logs:file_handler.show_debug(date)
file_handler.show_info(date)
file_handler.show_warning(date)
file_handler.show_error(date)While will show the corresponding message of the date. The default date istime.strftime('%Y-%m-%d', time.localtime(time.time()))，
which means today.Githubhttps://github.com/darkwhale/day_log
Package: dailylog1
dailylogOverviewI have many scripts run by cron and report errors via cron's email. This is great until
one of the scripts that runs frequently starts failing. Rather than waking to a full
mailbox, using this tool will throttled specific errors to a log and only show the error
every 24 hours by default. The time can be adjusted by passing a parameter.InstallationCheck out this repo and cd into the project directory and run:pipinstalldailylog1Usagedailylog--help
Usage:dailylog[OPTIONS]COMMAND[ARGS]...Entrypointforclickscript.

Options:-C,--cacheTEXTspecifyalternatecachefile(default~/.cache/dailylog.json)-c,--configTEXTspecifyalternateconfigfile(default~/.config/dailylog.yaml)-d,--debugincrementdebuglevel-t,--test/--no-testspecifytestmode-v,--verboseincrementverbositylevel-V,--versionshowversionandexit-h,--helpShowthismessageandexit.

Commands:logLoganerror.set-default-logSetanewdefaultlog.dailyloglog--help
Usage:dailyloglog[OPTIONS]Logamessage.

Options:-kTEXTSpecifykey[required]-mTEXTSpecifymessage[required]-sINTEGERSpecifysecondstosuppress(default86400[oneday])-lINTEGERSpecifyoneoflogginglevels(default:logging.ERROR)-fTEXTSpecifyalternatelogfile-h,--helpShowthismessageandexit.dailylogset-default-log--help
Usage:dailylogset-default-log[OPTIONS]LOG_FNSetanewdefaultlog.

Options:-h,--helpShowthismessageandexit.DocumentationStableLatestLicenseMIT
Package: dailypy
Useful Python Functions used almost everyday.# Install`bash pip install dailypy `# Useage`python import dailypy as dp time = dp.sm.timeString() `Change Log0.0.1 (19/08/2020)First Release0.0.2 (19/08/2020)Update README for better viwing0.0.3 (20/08/2020)Change src to dailypy0.0.4 (22/12/2020)Add timeString function in sm (string manipulate) class.
Package: daily-python
Daily Client SDK for PythonThe Daily client SDK for Python allows you to build video and audio calling into your native desktop and server applications.This SDK is well suited to build AI applications on the server side as it can be easily integrated with well-known Python libraries such asYOLOv5,PyTorch,OpenCV, ornumpy.The SDK's core features include:Joining a Daily meeting as a participantAs a meeting participant, configuring inputs, publishing, and subscription settingsReceiving video frames from other participantsReceiving raw audio from all participants in the meetingSending video into a meetingSending raw audio into a meetingThis functionality can be applied to several AI use cases, including:Performing object or face detection on the server sideProcessing audio from a Speech-To-Text platform into a meetingSending audio from a Text-To-Speech platform into a meetingSending video and audio tracks to a content moderation platformUsing generative AI to inject video content into a meetingDocumentationSee theDaily Python API docs.For demos on how to usedaily-python, refer to thedemosdirectory.Installationdaily-pythoncan be easily installed usingpip:pipinstalldaily-pythonTo upgrade:pipinstall-Udaily-pythonRequirementsPython 3.7 or newerUsageFor usage details, visit Daily'sPython SDK getting start guide.SupportNeed help or have feedback? You can reach out through ourdeveloper communityorchatwith our support engineers.
Package: daily-reflection
Daily ReflectionA small python, command line tool that helps you conduct your daily self-reflection, morning and or evening. It allows you to define questions that you want to ask to your selves and stores your daily answers in a password protected storage.InstallationVia pippip install daily-reflection
daily-reflectionVia pip + gitpip install git+git://github.com/stefanthaler/daily-reflection.git#egg=daily-reflection
daily-reflectionVia gitgit clonegit@github.com:stefanthaler/daily-reflectionpython3 -m venv .env. .env/bin/activate.fishpip3 install -r requirements.txt./daily-reflectionBuilding packagegit clonegit@github.com:stefanthaler/daily-reflectionpython3 -m venv .env. .env/bin/activate.fishpip3 install -r requirements-building.txtpython setup.py bdist_wheelOS Dependencies:python3, python3-venv, python3-pipPython Dependencies:PyInquirerTinyDBtinydb-encrypted-jsonstorageptpythonPython Dependencies Building:setuptoolswheeltqdmtwineThanks to:Deepak Kumar for his tutorial on how to publish your python package on pip,https://dzone.com/articles/executable-package-pip-installShields.io, for providing the MIT  license button:https://shields.io/
Package: daily-summary
daily-summaryGenerate a summary of your commit history for the day.Usage:export OPENAI_API_KEY=your-api-keydaily-summary --repo relative/path/to/your/repo --author "Author Name" --model--model is an optional argument that specifies the OpenAI model to be used. Defaults to "gpt-4" if not provided.
--date is optional but lets you create reports for different dates than today"Author Name" should match your git commit author namePublishing:Build the package:python3 publish.pyORpython3 setup.py sdist bdist_wheel
Update the version number in setup.py
pip install twine
twine upload dist/*
username = _tokenpassword = PYPI_TOKEN
Package: dailytreasuryrates
Daily Treasury Rates DownloaderA simple command line tool for downloading or updating daily US interest rates (Par Yield Curve Rates) from the US Department of the Treasury.InstallationTo install run:> pip install dailytreasuryratesUsageTo create or update the filerates.csvwith the latest treasury rates, run the following command:> dailytreasuryrates rates.csvThis will look for therates.csvfile in the current folder and append any new data that is available for download on the US treasury site. You can also specify a fully qualitied path likeC:\dataset\myrates.csvif you want to maintain the rates files at a different location.A common usage for the rates-file it to load it into Python with Pandas for processing:import pandas as pd

df = pd.read_csv('rates.csv', parse_dates=["Date"]).set_index("Date")Output format detailsThe output file is acsvfile.Date,1 Mo,2 Mo,3 Mo,4 Mo,6 Mo,1 Yr,2 Yr,3 Yr,5 Yr,7 Yr,10 Yr,20 Yr,30 Yr
1990-01-02,,,7.83,,7.89,7.81,7.87,7.9,7.87,7.98,7.94,,8.0
1990-01-03,,,7.89,,7.94,7.85,7.94,7.96,7.92,8.04,7.99,,8.04
...
2023-03-06,4.75,4.79,4.93,5.02,5.22,5.05,4.89,4.61,4.27,4.16,3.98,4.14,3.92
2023-03-07,4.8,4.88,5.04,5.12,5.32,5.22,5.0,4.66,4.31,4.17,3.97,4.11,3.88The first columns contains the date inyyyy-mm-ddformat.Dates are sorted in ascending order.The first date is Jan 2nd 1990.The number of tenors has grown over time. E.g. in 1990 there was no1 Monthand2 Monthtennor, but now there is. These missing values for older dates have empty string in the csv file, as can be seen in the snipped above.Download detailsData gets downloaded from the U.S. DEPARTMENT OF THE TREASURY
: Daily Treasury Par Yield Curve Rateshttps://home.treasury.gov/resource-center/data-chart-center/interest-rates/TextView?type=daily_treasury_yield_curve
Package: dainik
DainikDainikis a client library for NimbleBox.ai LMAO (Logging, Monitoring, Alerts and Observability) service.UsagefromdainikimportDainik# initialise the client objectdk=Dainik()# initialise the rundk.init("california",config={"model_name":"qual","config":{"batch_size":32,"n_steps":n_steps,"optimizer":"adam",}},)forepochinrange(10):log={"loss":1/epoch,"accuracy":epoch,}dk.log(log,step=epoch)# optionally log metrics directlydk.metrics.f1_score(x=[[1,2,3]],y=[[1,2,1]],step=epoch)# log files for tracking as well, wildcards work as well!dk.save_files([f"./checkpoint-{epoch}/*"])dk.end()# send signal to sleepDevDue to the current structure the source code for this is only available for NimbleBox.ai engineers. This is spun out of a much larger monorepo which contains the logic for server and client together which helps us work on this faster.
Package: daiquiri-rollbar
daiquiri-rollbarEasy way to useRollbarwithdaiquiri.The level of the messages is preserved. Sologger.info('smth')will be registered asinfoin Rollbar. If you configure daiquiri to log messages above a certain level, only those messages will be sent to Rollbar. You can also configure theRollbarOutputto only log messages for a level that is different from the global configuration.Exception should be logged withlogger.exception. The stacktrace will then be sent to Rollbar withreport_exc_info.To use it, just addRollbarOutputto the list of daiquiri outputs and use daiquiri normally:importloggingfromdaiquiri_rollbarimportRollbarOutputrollbar_output=RollbarOutput(access_token='access_token')daiquiri.setup(level=logging.INFO,outputs=(rollbar_output,))logger=daiquiri.getLogger(__name__)logger.info('Test')To log message of a certain level:importloggingfromdaiquiri_rollbarimportRollbarOutputrollbar_output=RollbarOutput(access_token='access_token',level=logging.WARNING)daiquiri.setup(level=logging.INFO,outputs=(rollbar_output,))logger=daiquiri.getLogger(__name__)logger.info('Test')By default, log messages will be logged for production. To change the environment, buildRollbarOutputlike this:rollbar_output=RollbarOutput(access_token='access_token',environment='development')If you don’t pass an access token,RollbarOutputwill not log anything.Error messages from therollbarmodule are not sent into Rollbar to prevent an infinite loop. They are sent to stderr instead. They will be logged by other outputs if you have any.Changelog0.2.1 (2017-08-15)Improve documentation0.2.0 (2017-08-15)Log messages into rollbar
Package: daisybell
daisybellA scanner that will scan your AI models for problems. Currently it focuses on bias testing. It is currently alpha.How to UseFirst install it:pip install daisybellRun it in this manner (currently supports models from HuggingFace’s repository):daisybell roberta-baseThe scan can output files for further analysis:daisybell roberta-base --output results/roberta-baseWe will infer the task(s) of model by default but to provide specific tasks to test explicitly use the –task switch:daisybell cross-encoder/nli-distilroberta-base --task zero-shot-classificationThat’s it for now. More will come.Future WorkMore bias tests. More metrics for bias testing based on the research in the field.Integration with other types of testing (eg. adversarial robustness)More kinds of models besides HuggingFace models. We are especially interested in MLFlow integration.Documentation.Please contribute if you can. Help is always helpful.LicenseApacheCreditA project of IQT Labs.
Package: daisydukes
Stylish transport for your pythong projectsDaisyDukes can:Create archives of your project:supports tar.(gz, bz2, lzma, xz)Upload your project someplace:Supports S3, FTP, and PyPIPackage your project:Supports RPM and DEBCommands:daisydukes archive [–zip|gzip|bzip2|lzma|xz] [–extrafiles F…]
daisydukes package
daisydukes uploadDaisyDukes is a part of a set of utilities including:pythong     -https://github.com/oddshocks/pythongdaisydukes  -https://github.com/rossdylan/daisydukesnighty      -https://github.com/??/nighty
Package: daisyfl
Daisy - A Hierarchical Friendly Federated Learning Framework For Edge Computingdev mode (virtual environment)1. clone the source codegit clone https://github.com/Intelligent-Systems-Lab/daisy2. build up environmentprepare and activate your virtual environment (python=3.8^)cd daisy
./dev/bootstrap.shdevelopsetup examplesdon't overwrite daisyfl dependency in this step.cd <example_path>
pip install <pkgs_for_your_example>5. run examplesdev mode (docker)1. clone the source codegit clone https://github.com/Intelligent-Systems-Lab/daisy2. build up environmentdocker run -it -v <daisy_source_code>:/root/daisy tcfwbper/daisyfl-dev:<version_tag> /bin/bash3. develop4. setup examplesdon't overwrite daisyfl dependency in this step.docker attach <container_id>cd <example_path> && conda activate daisy
pip install <pkgs_for_your_example>5. run examplesuser mode1. install daisyflpip install <daisyfl_version>2. setup examplespip install <pkgs_for_your_example>3. run examples
Package: daisykit
Daisykit Pythonhttps://pypi.org/project/daisykit/Daisykit is an easy AI toolkit for software engineers to integrate pretrained AI models and pipelines into their projects. You DON'T need to be an AI engineer to build AI software. This open source project includes:Daisykit SDK - C++, the core of models and algorithms in NCNN deep learning framework.Daisykit Pythonwrapper for easy integration with Python.Daisykit Android- Example app demonstrate how to use Daisykit SDK in Android.How to install ?For Windows:pip3 install daisykitFor Ubuntu:Install dependenciessudo apt install pybind11-dev # Pybind11 - For Python/C++ Wrapper
sudo apt install libopencv-dev # For OpenCV
sudo apt install libvulkan-dev # Optional - For GPU supportInstall DaisyKit (compile from source)pip3 install --upgrade pip # Ensure pip is updated
pip3 install daisykitFor other platforms:Install OpenCV, Pybind11 and Vulkan development package (if you want GPU support)Install DaisyKit (compile from source)pip3 install --upgrade pip # Ensure pip is updated
pip3 install daisykitExamplesReadDocumentation.Note for Python buildCurrent CD (continuous delivery) flow is partial, which means we only have prebuilt linux wheels for x86_64 and for Windows.Prebuilt wheels for linux x86_64 are built with Github actions.Windows wheels (64bit) are built manually on a local machine.macOS prebuilt wheels are not available for now. However, you can install dependencies (OpenCV, Vulkan) manually, then install Daisykit with pip command.We will be happy if you can make a pull request to make the CD build fully automated. A good choice is using Github flow for all building tasks.Current steps for Windows build:bash./build_tools/py_windows/build_dk_all_pythons_windows.sh
bash./build_tools/py_windows/build_dk_python_source_dist.sh
bash./build_tools/upload_pypi.shBug reportPlease open an issue on our official repository if you find any error.https://github.com/nrl-ai/daisykit
Package: daisy-llm
🌼 Daisy LLM Tools 🌼Daisy is a Python platform designed to work with language model APIs such as OpenAI's GPT-3 and GPT-4. It includes a suite of classes and methods that can be used to converse with, extend capabilities, and augment the reasoning capacities of large language models.🏁 Getting StartedInstall packagepip install daisy_llmImport into your project and use. See main-example.pyCreateconfig.pyin your project with necessary information and enable desired modules to be loaded. SeeDaisy-openai-chatfor sample project and a collection of modules to get started🧰 CapabilitiesDaisy accepts different types of user-developed "modules". A voice assistant module comes withDaisy-openai-chatas a "proof-of-concept". Possible configurations and apps built using Daisy could include:Web appsConversational processing APIsComputer vision interpretationAutonomous initiation (with time awareness, Daisy could send a message or tool-form without user input)Customer service IVR (which could be powerful with API tool-forms on the back-end).Possibilities are endless, you only need to create what you want to see in the world. This platform makes that easier.Keep in mind: Daisy is still in development. It has, and will, evolve significantly in the coming months as contributors enhance functinality by improving platform code, ading module hooks, and developing their own modules.🛎️ ServicesDaisy uses the following APIs for conversation processing:Language model: OpenAI chatGPTSpeech-to-text (STT): AssemblyAIText-To-Speech(TTS): ElevenLabs (Quality), Google Cloud TTS (Cheap), Google Translate TTS (Free!) (Modules inDaisy-openai-chat. Uses pyttsx3 by default)Wake word (Local): Picovice PorcupineAlternative local APIs are available and should be easily interchangeable if you choose to use them. In some cases, they can be switched out as modules. In every other case, a code hook can be added to make it interchangeable.🌇 BackgroundI have been eager to have a conversation with chatGPT using my voice. I used to search daily for a program that could exchange between speech recognition and TTS for a real human-like conversation, but it was not until recently that I discovered one.So of course I began making what I wanted in the world. I started working on a voice recognition script for chatGPT. It began with simple requests, such as incorporating a request to openAI API and routing the speech recognition output. Since then the project evolved into a platform for building applications, opening the door for infinite potential.Some people argue that text models and AI are not thinking, but just using heuristics. However, when we examine ourselves, we too are simply a collection of learned behavior and responses. Although GPT may not be perfect, it is important to reflect on ourselves and determine how much better we truly are.🤝 CompatibilityThis software is designed to run on Windows and Linux.✅ To-DoLLMs (API or local) as modules
Package: daisypy-vis
daisypy-visVisualisation library and tools for Daisy model outputSeedocfor examples.InstallationThe visualization tools are available from pypipip install daisypy-visDevelopmentCheckout repositorygit clone git@github.com:daisy-model/daisypy-vis.git
cd daisypy-visInstall package as editablepip install -e ".[all]"pylintUsepyproject.tomlfor package-wide settings, e.g.ignore-trailing-whitespace.pylint daisypy/vis
pylint docTestsSeepyproject.tomlfor configuration.To install test dependencies, use either the[all]or the[test]target, e.g.pip install ".[test]"To run testspytestUse pytest-mpl to compare images. Generate baselines images by runningpytest --mpl-generate-path=test-data/baselineand inspect the output...Note that image comparison tests can fail between different version of matplotlib and freetype. You can force test against images generated with a specific version of matplotlib withpytest --mpl-baseline-path=test-data/baseline/matplotlib-<matplotlib-version-number>If no baseline images are available for a specific version, you can generate withpytest --mpl-generate-path=test-data/baseline/matplotlib-<matplotlib-version-number>and compare manually.Baseline for animations are generated by running the tests, this will produce a new set of images intest-data/tmp. Inspect and move totest-data/baselinewhen happy. In general, text should be removed from to avoid tests failing due to slight variations in font rendering. See for examplerender_and_compare_animationindaisy_vis/animate/test/test_animate_depth_timeseries.pyCoverageSeepyproject.tomlfor configuration. To generate coverage reportcoverage runTo inspect coverage reportcoverage reportTests should as a minimum have 100% code coverage. When applicable, fixed tests should cover boundary conditions and randomized tests should sample the full parameter space.
Package: daisys
Daisys APIThis library wraps the Daisys API for text-to-speech and voice generation services.Have your product talking in seconds!withDaisysAPI('speak',email='user@example.com',password='pw')asspeak:voice=speak.get_voices()[-1]print(f"{voice.name}speaking!")take=speak.generate_take(voice_id=voice.voice_id,text="Hello there, I am Daisys!",prosody=SimpleProsody(pace=-3,pitch=2,expression=10))audio_wav=speak.get_take_audio(take.take_id,filename='hello_daisys.wav')This library usespydanticandhttpxas the main dependencies.  Clients using normal,
synchronous Python, or usingasyncio, are available.Please visit theonline documentationfor information on how to use the Daisys API from Python, or from any language using the
documented REST endpoints.A product ofDaisys AI.The software is licensed with the MIT license, as detailed in LICENSE.
Package: daj
[![Build Status](https://travis-ci.org/Yomguithereal/python-daj.svg)](https://travis-ci.org/Yomguithereal/python-daj)# dajAre you terminally annoyed by the number of unaesthetic lines you have to write when the only thing you need is just reading and parsing some fracking data?Well if this is the case, **daj** is made for you!**daj**'s philosophy is to enable you to read and write popular data formats in one simplistic line.No more of the following nonsense:```pythonimport codecsimport jsonwith codecs.open('path/to/your-fracking-data.json', 'r', encoding='utf-8') as jf:data = json.load(jf)```Now, you just write:```pythonfrom daj import dajdata = daj < 'path/to/your-fracking-data.json'```**daj** is your quick & dirty scripting companion. No more annoying boilerplate code: you start coding things that matter right now!## InstallationWith pip```pythonpip install daj```To install the latest dev version```bashpip install git+https://github.com/Yomguithereal/python-daj.git```## Formats supported* plain text* json* yaml* csv* tsv## ReadingNote that if you do not provide a method to **daj**, this one will try to guess your format by analyzing the file's extension.So if you have a twisted mind and like to name csv files `table.json`, well first of all you are a sneaky bastard, second, just use the proper **daj** method.```python# Guessing the formatdata = daj < 'file.json'# Will also work with raw textdata = daj < 'file.txt'# Using daj methodsdata = daj.json < 'file.json'data = daj.yml < 'file.yml'data = daj.csv < 'file.csv'data = daj.tsv < 'file.tsv'# Needing headers for your neat CSV files?data = daj.csvh < 'file.csv'data = daj.tsvh < 'file.tsv'```## WritingAs for reading, **daj** will try to guess the correct format for your data based on the extension of the file.```python# Writing some datadaj(data) > 'file.json'# Will also work with raw textdaj(data) > 'file.txt'# Using daj methodsdaj.json(data) > 'file.json'daj.yml(data) > 'file.yml'# If you want to ouput a pretty printed jsondaj.pjson(data) > 'prettyfile.json'# For csv, you can give an array of arrays or an array of objects# If an array of objects is given, daj will output a csv with headers.daj.csv(data) > 'file.csv'daj.tsv(data) > 'file.tsv'```## Disclaimer**daj** is clearly orientated toward quick & dirty data processing. This is probably a bad idea to use it in a production context and I would not vouch for that.## ContributionContribution are more than welcome, be sure to add relevant unit tests and pass them all before subitting any code.```bash# To install dev environment (preferably in a virtualenv)pip install -r requirements.txt# To run unit tests./runtests```