Saravanan666/pypi-org huggingface dataset:
Package: parquet-csv
Parquet_CSV|PyPIA Parquet to and from CSV converter that is based onApache Arrowfor its speed and memory efficiency.How to installpipinstallparquet_csvUsepip3if both Python2 and Python3 are installed. This application only works with Python3.How to useConverting Parquetparquet_to_csvconvertsparquetfiles tocsvfiles. By default it prints to the standard
output, but can be directed via pipe or-oflag to write to a file.Usage: parquet_to_csv.py [OPTIONS] INPUT_FILE

Options:
  -o, --output-path FILE  [default: (standard output)]
  --header / --no-header
  --verbose BOOLEAN
  --help                  Show this message and exit.Selecting columns,gzip-ing outputFollowing UNIX principle, you should be usingxsvfor selecting
columns from the csv or do other transformations: just pipe the output toxsvand you're all set.Similarly if you'd want the file to be compressed, pipe the result togzipand direct to a local
file ending in.csv.gz.
Package: parquet-metadata
parquet-metadataDump metadata about a Parquet file. You may also be interested incsv2parquet.sudo pip install parquet-metadata
parquet-metadata parquet.fileSample output:file	created_by	parquet-cpp version 1.4.1-SNAPSHOT
file	columns	9
file	row_groups	1
file	rows	2
row_group	0		size	634
row_group	0		rows	2
row_group	0		columns	9
row_group	0	bool	type	BOOLEAN
row_group	0	bool	num_values	2
row_group	0	bool	compression	SNAPPY
row_group	0	bool	encodings	PLAIN,RLE
row_group	0	bool	compressed_size	36
row_group	0	bool	uncompressed_size	34
row_group	0	bool	stats:min	False
row_group	0	bool	stats:max	True
row_group	0	float32	type	FLOAT
row_group	0	float32	num_values	2
row_group	0	float32	compression	SNAPPY
row_group	0	float32	encodings	PLAIN_DICTIONARY,PLAIN,RLE
row_group	0	float32	compressed_size	68
row_group	0	float32	uncompressed_size	64
row_group	0	float32	stats:min	0.5
row_group	0	float32	stats:max	0.6
[...]
Package: parquet-to-hyper
Parquet to HyperPackage to convert parquet files into a single hyper file.BenchmarkingThe package was benchmarked using a Latitude 3420 Dell Notebook with 16GB of Memory RAM, a 250GB SSD, and an i7-1165G7 CPU. The time may vary using different architectures. The table used for benchmarking contained60 columns. Although the tests were carried with a maximum of 500 million of rows, the package supports higher amount of volume. The limitation is only to the size of a single parquet file (up to 30GB). For larger volumes, it's recommended to split them into multiple parquet files. Follow the results:Rows (in millions)Time (in seconds)Parquet size (in MegaBytes)14.05541036.8520100412.649005002669.2525400How to useInstallationpipinstallparquet-to-hyperInitializing objectfrompackages.hyper_fileimportHyperFileparquet_folder='/path/to/your/folder'# The folder where the parquet files areparquet_extension='parquet'# Optional. Don't use it if the parquet files has no extensionhf=HyperFile(parquet_folder,parquet_extension)Create a single filehyper_filename='path/to/your/db.hyper'# Path to save hyper file with filenamerows=hf.create_hyper_file(hyper_file_name)print(f'Hyper created with{rows}rows.')Deleting rows from an existing hyper fileThis function deletes rows based on a control column (date column) and the days to delete from current day.hyper_filename='path/to/your/db.hyper'# Path to load hyper file with filenamecontrol_column='date_column'days=7hf.delete_rows(hyper_filename)print(f'{rows}rows were deleted.')Appending rows from parquet into an existing hyper filehyper_filename='path/to/your/db.hyper'# Path to load hyper file with filenamerows=hf.append_rows(hyper_filename)print(f'{rows}were appended.')Publishing hyper file into Tableau serverfrompackages.hyper_fileimportHyperFiletsu=TableauServerUtils(tableau_address,token_name,token_value)project_id=tsu.get_project_id(project_name)tsu.publish_hyper(project_id,'test.hyper')
Package: parquet-tools
parquet-toolsThis is a pip installableparquet-tools.
In other words, parquet-tools is a CLI tools ofApache Arrow.
You can show parquet file content/schema on local disk or on Amazon S3.
It is incompatible with original parquet-tools.FeaturesRead Parquet data (local file or file on S3)Read Parquet metadata/schema (local file or file on S3)Installation$pipinstallparquet-toolsUsage$parquet-tools--help
usage:parquet-tools[-h]{show,csv,inspect}...

parquetCLItools

positionalarguments:{show,csv,inspect}showShowhumanreadbleformat.see`show-h`csvCatcsvstyle.see`csv-h`inspectInspectparquetfile.see`inspect-h`optionalarguments:-h,--helpshowthishelpmessageandexitUsage ExamplesShow local parquet file$parquet-toolsshowtest.parquet
+-------+-------+---------+|one|two|three||-------+-------+---------||-1|foo|True||nan|bar|False||2.5|baz|True|+-------+-------+---------+Show parquet file on S3$parquet-toolsshows3://bucket-name/prefix/*
+-------+-------+---------+|one|two|three||-------+-------+---------||-1|foo|True||nan|bar|False||2.5|baz|True|+-------+-------+---------+Inspect parquet file schema$parquet-toolsinspect/path/to/parquetInspect output############ file meta data ############
created_by: parquet-cpp version 1.5.1-SNAPSHOT
num_columns: 3
num_rows: 3
num_row_groups: 1
format_version: 1.0
serialized_size: 2226


############ Columns ############
one
two
three

############ Column(one) ############
name: one
path: one
max_definition_level: 1
max_repetition_level: 0
physical_type: DOUBLE
logical_type: None
converted_type (legacy): NONE

############ Column(two) ############
name: two
path: two
max_definition_level: 1
max_repetition_level: 0
physical_type: BYTE_ARRAY
logical_type: String
converted_type (legacy): UTF8

############ Column(three) ############
name: three
path: three
max_definition_level: 1
max_repetition_level: 0
physical_type: BOOLEAN
logical_type: None
converted_type (legacy): NONECat CSV parquet and transformcsvq$parquet-toolscsvs3://bucket-name/test.parquet|csvq"select one, three where three"+-------+-------+|one|three|+-------+-------+|-1.0|True||2.5|True|+-------+-------+
Package: parquet-to-root
parquet_to_rootSimple translator from Parquet to ROOT TTreeInstall:pip3 install parquet_to_rootRequiresPyROOTandpyarrow. The latter can be installed viapip; the former is more complicated, but if you're reading this you probably know how to get it.  (The tests in this package use miniconda:conda install -c conda-forge root.)To run from the command line:python3 -m parquet_to_root infile.parquet outfile.root [-t TREENAME] [--verbose]To run from your script,from parquet_to_root import parquet_to_root
    parquet_to_root(parquetfile, rootfile, treename='parquettree', verbose=False)where the default values are shown for the optional arguments.Theparquetfileargument can either be a filename string or file-like object opened in binary mode. Therootfileargument can either be a filename string or an openTFile; if a string is provided any existing file will be overwritten, while if aTFileobject is provided the new tree will be added to the existing file.
Package: parrec-reader-py
parrec-reader-pyparrec-reader is a python library to read Philips par/rec files. XML/rec is not supported yetInstallationUse the package managerpipto install parrec-reader.pipinstallparrec-reader-pyCurrently parrec-reader supports python 3.6 and 3.7.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: parrot1
PARRoT: Precise Audio Recognition and Recap over TranscriptionA tool for writing a recap mail or report from a video recording of a call.InstallationPARRoT can be installed from pyPI by simply doing:python-mpipinstallparrot1with the following extras:faster-whisper-> to install faster-whisper dependenciesllama-cpp-> to install llama-cpp dependenciesos-models-> to install both the previous onesdocx-> to use external transcriptionall-> install everythingFor instance:python-mpipinstallparrot1[os-models]NOTE:PARRoT requiresFFmpegavailable and visible in the system!UsageYou can find all the information needed for using the tool by doingparrot --helpAt the moment, there are two commands:parrot mail <video_path>andparrot report <video_path>Options:--transcript (-t) <PATH>: path to the transcript with speakers (Optional).--output-filepath (-o) <PATH>: path to the final result (Optional, by default it creates the file in the current working directory).--faster-whisper (-fw): If set, it uses faster whisper instead of hosted OpenAI Whisper.--llama-cpp (-lc): If set, it uses llama-cpp instead of the hosted OpenAI GPT model.ContributingFirst of all make sure you have installed pre-commit and can use it.pip install pre-commitandpre-commit installYou can't push your code directly in the contrib branch. You should open a branch (feat|fix)/[/] and then open a PR with reviewers.Please try sticking to the code style or, for any eventual suggestion for libraries/patterns, discuss it before in a issue.
Package: parrot-api-core
Parrot Api CorePython API Project FrameworkFree software: MIT licenseDocumentation:https://parrot-api-core.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-12-04)First release on PyPI.
Package: parrot-feeder
General InformationAdvanced multichannel file sharing web server.See also:https://hub.docker.com/r/lep0puglabs/parrot-feederhttps://pypi.org/project/parrot-feeder/Parrot-feeder is an all-in-one combination of the following tools:https://github.com/cyberhexe/ngflaskhttps://github.com/mtalimanchuk/flask-fileboxhttps://github.com/mtalimanchuk/file-squire-botIt makes file transferring over Internet easier by giving you a tool to share a local directory through the Ngrok network.
It lists the given directory, creates a local web server with Flask, make this server sharing the listed files and creates an Ngrok tunnel to the port used by the Flask server.In other words, parrot-feeder temporarily exposes your files for downloading on the Internet.
Once you stop parrot-feeder, your files stop being shared over Ngrok.It also exposes an HTML page under the/api/uploadpath with a form for uploading files to the remote machine.If you supply the tool with a telegram bot token via the--telegram-bot-tokenargument or
via theTELEGRAM_BOT_TOKENenv variable,
you will also activate a telegram bot that will/fetchor/tailfiles for you from remote.Note-1: you also need to supply the--telegram-bot-whitelistorTELEGRAM_BOT_WHITELISTenv variable to specify the users allowed to interact with the bot.Note-2: you may need to sign up for Ngrok for tunneling HTML pages.Screenshots or it didn't happen.InstallationDownload the Docker image:dockerpulllep0puglabs/parrot-feeder:latestor build the Docker image yourself:dockerbuild-tparrot-feeder-f./Dockerfile.Run the Docker image:dockerrun--rm-it-p4200:4200parrot-feederOr install the package using PyPI:sudopip3installparrot-feederAfter starting the server, you can navigate to the following URLs:/ - for seeing the listing/api/upload - for accessing the API for uploading filesUsageServe files and folder from the current working directoryparrot-feederordocker run --rm -it -p 4200:4200 parrot-feederServe files and folders from the /tmp directoryparrot-feeder --directory /tmpPrint served files to the console on startupparrot-feeder --directory /tmp -pfBind the server to the given addressparrot-feeder --ip 10.10.10.10 --port 5050Print helpparrot-feeder -hOn startup print the actual files being shared over Ngrokparrot-feeder -pfUse a telegram bot to serve you files from the remote server:parrot-feeder --telegram-bot-token token --telegram-bot-whitelist username1,username2
Package: parrot-felindra
Felindra - Parrot Media Signature Verifier toolThefelindraPython module provides both a library and an executable
to verify the authenticity and integrity of any signed photo taken by
anANAFI Ai. The tool can verify photos remotely on a drone by using the
webserver API or offline, by checking a directory and its subdirectories.For more information, see thefelindra documentation.Felindra DocumentationParrot developers forumsParrot Anafi Ai:https://forum.developer.parrot.com/c/anafi-ai/Supported platformTested with Python 3.8.10. It should work on newer versions.
Package: parrot-gpt
The Parrot-GPT package provides a Python library that can convert any
bibliographic metadata between different schemas using the OpenAI Large
language models via its API. There is no limitation in the schemas that
can be transformed but currently there a restriction has been included
to input and output schemas.Free software: MIT licenseDocumentation:https://parrot-gpt.readthedocs.io.Metadata FormatsSchemaExampleDATScffcrossref_xmlJATSBioSchemaCodemetaRIF-CSEDMIDCATDCAT-APDataCiteDataCite-XMLDataCite-JSONCrossrefschema.orgbibtexDC-XMLDC-JSONDublin CoreInstallationYou can installparrot_gptwithpip:$ pip install parrot_gptUsage from the command line$exportOPENAI_API_KEY={OPENAI_API_KEY}$parrot_gpt.cli-m./path/to/your/filename-icrosssref-tdatacite>outputHistory0.1.0 (2023-02-19)First release on PyPI.
Package: parrot-integrations-core
Parrot Api CorePython API Project FrameworkFree software: MIT licenseDocumentation:https://parrot-integrations-core.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-12-04)First release on PyPI.
Package: parrot-integrations-gcp
Parrot Api CorePython API Project FrameworkFree software: MIT licenseDocumentation:https://parrot-integrations-gcp.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-12-04)First release on PyPI.
Package: parrotjoy
parrotjoy - a pygame 2 project.For making music, and visuals.https://github.com/pygame/parrotjoyGet started.Installationpython3 -m venv venv
. ./venv/bin/activate
pip install -r requirements.dev.txt

parrotjoyJoypad controller instructionsThere are 4 audio tracks.
The first two tracksPressing the numbered buttons (1,2,3,4) in combination withmodifierones.R1 ) track.start_new_nextR2 ) track.add_to_nextL1 ) track.eraseIf recording, track.finish()
else:         track.play()Trimming with analog joy sticks.The analog controllers can be used to trim a track.
This is useful if you record too much sound, or you want to line it up with other parts.Left stick trims the start of the track. Tap the left stick to the right to have less of the sound from the beginning.Right stick is for trimming the end of the track.Keyboard control.s, start recording a new trackS, record over the top of an existing trackf, finish recording track.p, playw, trim the START of the sound. Get MORE sound on LEFT of sound.e, trim the START of the sound. Get LESS sound on LEFT of sound.r, trim the END of the sound. Get LESS sound on RIGHT of sound.t, trim the END of the sound. Get MORE sound on RIGHT of sound.space, adjust the BPM timer. Tap it in time until the BPM is as you like.'0', use Looper scene.'9', use VideySynth scene.'8', strawberries.Running tests.Tests can be found in the tests/ folder.Typepytest.
Or runpython -m toxTests are run on mac, linux, windows when there is a pull request made.releasingReleasing is tested with python3.7(not python2 or any other version).To the python package index (pypi).rm -rf dist/*
python setup.py sdist bdist_wheel
twine upload dist/*On windows:python setup.py bdist_msi
dir build/*.msiOn mac:python setup.py bdist_dmg
ls build/*.dmgLicensesCode licenseLicense for code will be the same as the pygame license (LGPL).Check the licenses for the dependencies.parrotjoy usesmany parts made by others...pygamepythonaubiolibsdl... numpy, opencv-python, pytest, cxfreeze
Package: parrot-olympe
Olympe - Python controller library for Parrot DronesOlympe provides a Python controller programming interface for Parrot Drone. You can use Olympe to
connect and control a drone from a remote Python script executed from your computer.
Olympe is primarily intended to be used with a simulated drone (using Sphinx, the Parrot drone
simulator) but may also be used to connect to physical drones. Like GroundSDK-iOS and
GroundSDK-Android, Olympe is based on arsdk-ng/arsdk-xml.Olympe is part of theParrot Ground SDKwhich allows any developer
to create its own mobile or desktop application for ANAFI, ANAFI Thermal and ANAFI AI drones.Olympe DocumentationOlympe - InstallationOlympe - User guideOlympe - API ReferenceOlympe - SDK Messages ReferenceSphinx DocumentationSphinx - System requirementsSphinx - InstallationSphinx - Quick Start guideParrot developers forumsParrot Anafi:https://forum.developer.parrot.com/c/anafi/Parrot Anafi Ai:https://forum.developer.parrot.com/c/anafi-ai/LicenseSPDX-License-Identifier: BSD-3-ClauseOlympe source code is licensed under the BSD-3-clause license, some part incorporated into Olympe
coming from CPython/asyncio are licensed under their original PSF-2.0 license agreement.Supported platformLinux Desktop PC (Ubuntu, Debian)
Package: parrot-rcc
parrot-rccmimicshttps://pypi.org/project/carrot-rcc/for Zeebe.parrot-rccis Work in progress.Usage: parrot-rcc [OPTIONS] [ROBOTS]...

  Zeebe external task Robot Framework RCC client

  [ROBOTS] could also be passed as a space separated env RCC_ROBOTS

Options:
  --rcc-executable TEXT
  --rcc-controller TEXT
  --rcc-fixed-spaces BOOLEAN
  --rcc-telemetry BOOLEAN
  --task-timeout-ms INTEGER
  --task-max-jobs INTEGER
  --zeebe-hostname TEXT
  --zeebe-port INTEGER
  --camunda-client-id TEXT
  --camunda-client-secret TEXT
  --camunda-cluster-id TEXT
  --camunda-region TEXT
  --log-level TEXT
  --help                        Show this message and exit.
Package: parrots
🇨🇳中文|🌐English|📖文档/Docs|🤖模型/ModelsOnline DemoParrots: ASR and TTS toolkitIntroductionParrots, Automatic Speech Recognition(ASR), Text-To-Speech(TTS) toolkit, support Chinese, English, Japanese, etc.parrots实现了语音识别和语音合成模型一键调用，开箱即用，支持中英文。FeaturesASR：基于distilwhisper实现的中文语音识别（ASR）模型，支持中、英等多种语言TTS：基于GPT-SoVITS训练的语音合成（TTS）模型，支持中、英、日等多种语言Installpipinstalltorch# or conda install pytorchpipinstall-rrequirements.txt
pipinstallparrotsorpipinstalltorch# or conda install pytorchgitclonehttps://github.com/shibing624/parrots.gitcdparrots
pythonsetup.pyinstallDemoOfficial Demo:https://www.mulanai.com/product/asr/HuggingFace Demo:https://huggingface.co/spaces/shibing624/parrotsrun example:examples/tts_gradio_demo.pyto see the demo:pythonexamples/tts_gradio_demo.pyUsageASR(Speech Recognition)example:examples/demo_asr.pyimportosimportsyssys.path.append('..')fromparrotsimportSpeechRecognitionpwd_path=os.path.abspath(os.path.dirname(__file__))if__name__=='__main__':m=SpeechRecognition()r=m.recognize_speech_from_file(os.path.join(pwd_path,'tushuguan.wav'))print('[提示] 语音识别结果：',r)output:{'text': '北京图书馆'}TTS(Speech Synthesis)example:examples/demo_tts.pyimportsyssys.path.append('..')fromparrotsimportTextToSpeechm=TextToSpeech(speaker_model_path="shibing624/parrots-gpt-sovits-speaker-maimai",speaker_name="MaiMai",device="cpu",half=False)m.predict(text="你好，欢迎来北京。welcome to the city.",text_language="auto",output_path="output_audio.wav")output:Save audio to output_audio.wav命令行模式（CLI）支持通过命令行方式执行ARS和TTS任务，代码：cli.py> parrots -h                                    

NAME
    parrots

SYNOPSIS
    parrots COMMAND

COMMANDS
    COMMAND is one of the following:

     asr
       Entry point of asr, recognize speech from file

     tts
       Entry point of tts, generate speech audio from textrun：pipinstallparrots-U# asr exampleparrotsasr-h
parrotsasrexamples/tushuguan.wav# tts exampleparrotstts-h
parrotstts"你好，欢迎来北京。welcome to the city."output_audio.wavasr、tts是二级命令，asr是语音识别，tts是语音合成，默认使用的模型是中文模型各二级命令使用方法见parrots asr -h上面示例中examples/tushuguan.wav是asr方法的audio_file_path参数，输入的音频文件（required）ContactIssue(建议)：邮件我：xuming:xuming624@qq.com微信我：加我微信号：xuming624, 进Python-NLP交流群，备注：姓名-公司名-NLPCitation如果你在研究中使用了parrots，请按如下格式引用：@misc{parrots,
  title={parrots: ASR and TTS Tool},
  author={Ming Xu},
  year={2024},
  howpublished={\url{https://github.com/shibing624/parrots}},}License授权协议为The Apache License 2.0，可免费用做商业用途。请在产品说明中附加parrots的链接和授权协议。Contribute项目代码还很粗糙，如果大家对代码有所改进，欢迎提交回本项目，在提交之前，注意以下两点：在tests添加相应的单元测试使用python -m pytest来运行所有单元测试，确保所有单测都是通过的之后即可提交PR。ReferenceASR(Speech Recognition)EAT: Enhanced ASR-TTS for Self-supervised Speech RecognitionPaddlePaddle/PaddleSpeechNVIDIA/NeMoTTS(Speech Synthesis)coqui-ai/TTSkeonlee9420/Expressive-FastSpeech2TensorSpeech/TensorflowTTSRVC-Boss/GPT-SoVITS
Package: parsable
Parsable is a lightweight decorator-based command line parser library.
Parsable was written to be simpler than argparse, optparse, and argh.InstallationInstall fromPyPIwithpippipinstallparsableOr just downloadparsable.pyand add to your project.UsageParsable uses just two pieces of syntax: a@parsablecommand decorator,
and aparsable()dispatch function.Import parsable.fromparsableimportparsableDecorate functions you want parsed.
Parsable inspects the function to decide how to parse arguments.
Arguments without default values are parsed as strings.
Default arguments of any typeTcan be parsed as long
asT(some_string)can do the parsing.@parsabledefmy_function(required_arg,optional_bool=True,optional_int=1):'''Help messages are not just a good idea, they are required'''# parsable automatically converts types based on default arguments:assertisinstance(required_arg,str)assertisinstance(optional_string,bool)assertisinstance(optional_int,int)# ...@parsabledefdo_stuff_with_files(*filenames,inplace=True):'''This does something to each file'''# ...Dispatch at the end of the script.if__name__=='__main__':parsable()Use your new script$pythonmy_script.pymy_functiondemooptional_int=5...# parsable replaces - with _ to make functions easier to read$pythonmy_script.pydo-stuff-with-files*.pyinplace=false...Advanced UsageTo show verbose information (commmand name and timing info),
set the environment variablePARSABLE_VERBOSE=true.If you use parsable for many modules in a package, you can collect them in yoursetup.pyusingparsable.find_entry_points().fromparsableimportparsablefromsetuptoolsimportsetupsetup(name='my_package',entry_points=parsable.find_entry_points('my_package'),...)LICENSEParsable is dual-licensed under the MIT and GPL2 licenses.
Package: parsac
# parsacparsac (formerly acpy) is a Python-based tool for sensitivity analysis and auto-calibration in parallel.
It is designed for analysis of models that take significant time to run.
For that reason, it focuses on storing and exploiting every single model result,
and performing model runs in parallel on either a single machine or
on computer clusters. It works with models that are run by calling one binary,
that use text-based configuration files based on YAML or Fortran namelists,
and that write their output to NetCDF.[![DOI](https://zenodo.org/badge/206791023.svg)](https://zenodo.org/badge/latestdoi/206791023)## Installationpip install parsac –userRemove–userto install in the system’s shared Python directory (not recommended).
Some systems have multiple versions of pip, e.g., pip for Python 2, pip3 for Python 3.
Make sure you use the command that corresponds to the Python version you want to install into.### Dependenciesparsac supports parallel simulations through [Parallel Python](https://www.parallelpython.com).
This package supports Python 2 out of the box (pip install pp –user), but its Python 3 version
is currently in beta. To install pp in Python 3, [download the zip file with the Python 3 port of Parallel Python](https://www.parallelpython.com/content/view/18/32), extract its contents, go to the contained directory and open a command prompt there, then runpython setup.py install.parsac uses [SALib](https://github.com/SALib/SALib) for sensitivity analysis. Typically, this can be installed withpip install SALib –user. If you are using [the Anaconda Python distribution](https://www.anaconda.com), you can instead doconda install SALib(you may need to add-c conda-forge).## Known issuesOn Windows, parallel runs may finish with several “ERROR: The process “xxx” not found.” messages. These are harmless and can be ignored - the analysis has completed successfully and all results have been correctly processed.