Saravanan666/pypi-org huggingface dataset:
Package: pneumatic
A Bulk-Upload Library for DocumentCloudpneumatic is a Python 3 library that adds some luxury and safeguards to the bulk-uploading of hundreds, thousands or hundreds of thousands of files toDocumentCloud. It is meant to do one thing – upload – and serve as an adjunct to, but not a replacement for, the excellentpython-documentcloudAPI wrapper.pneumatic’s name is inspired by the pneumatic dispatch systems in newsrooms of yore, which featured aseries of pneumatic tubesfor sending copy from the newsrooms to other departments such as the composing room.FeaturesCatalogs the API response for each upload in a SQLite database along with the file’s canonical URL.Post-processing, can update the SQLite database with each document’s page count and file hash.Dumps the SQLite data to a CSV if you wish.Prevents inadvertent submission of file types DocumentCloud doesn’t handle, such as audio.LinksDocumentation:https://pneumatic.readthedocs.io/en/latest/Repository:https://github.com/anthonydb/pneumaticIssues:https://github.com/anthonydb/pneumatic/issuesBasic UsageYou will need an active DocumentCloud account and Python 3.5+. First, install via pip:pipinstallpneumaticExample use: To upload all files in a directory (and all sub-directories below it), assign them to an existing project, set the files to public access, and tag each with metadata, run the following code:frompneumaticimportDocumentCloudUploaderuploader=DocumentCloudUploader('person@example.com','your-password')uploader.upload(file_directory='/govfiles',project='17477-loudoun-county-government',access='public',data={'type':'government','action':'lawsuit'})Please see thefull documentationfor more examples, including how to access the uploads database.
Package: pneuro
AutoNeuroAutoNeuro is an automated machine learning application built using python 3.7. It allows users to build production ready ML models with ease and efficiency.See theAbout uspage for more information.website:https://nabeelfahmi12.github.io/AutoNeuro-Documentation/InstallationDependenciesAutoNeuro requires:Python 3NumpyPandasscikit-learnUser InstallationIf you already have a working installation of numpy and Pandas, the easiest way to install autoNeuro is usingPippip install pneuroThe documentation includes more detailedinstallation instructionsChange logSee thechange logfor a history of notable changes to AutoNeuro.DevelopmentWe welcome new contributors of all experience levels. TheDevelopment Guidehas detaled information about contributing code, documentation, tests, and more.
We have included some basic information in README.Important linksOfficial source code repo:https://github.com/viratsagar/AutoneuroDownload releases:Issue Tracker:Source codeYou can check the latest sources with the command:git clone https://github.com/nabeelfahmi12/AutoNeuro-Documentaion.gitContributingTo learn more about making a contribution to scikit-learn, please see ourContributing guideTestingAfter installation, you can launch the test suite from outside the source directory (you will need to have pytest >= 5.0.1 installed):pytest AutoNeuroSubmitting a Pull RequestBefore opening a Pull Request, have a look at the full Contributing page to make sure your code
complies with our guidelines:https://nabeelfahmi12.github.io/AutoNeuro-Documentation/Contributingtoautoneuro/Contributingtoautoneuro/Help and Supporthttps://nabeelfahmi12.github.io/AutoNeuro-Documentation/Contributingtoautoneuro/Contributingtoautoneuro/DocumentationHTML documentation (stable release):HTML documentation (development version):FAQ:CommunicationMailing list:Twitter:https://twitter.com/Sudhans74624324Stack Overflow:Website:https://nabeelfahmi12.github.io/AutoNeuro-Documentation/CitationIf you use autoneuro in a scientific publication, we would appreciate citations:
Package: pngcanvas
A minimalist library to render PNG images using pure Python inspired by Thomas Fuchs’ spark_pr library, originally published here in 2005.Given that I get a couple of mails about it every quarter or so, and that it’s been consistently popular with the Google App Engine crowd, I decided to release it under the MIT license and welcome any contributions towards speeding it up, cleaning it up and making it generally more useful.So ping me if you want commit access (or just send me a pull request with any enhancements).FeaturesAs it stands, you can do the following things with aPNGCanvasinstance:Set single pixelsDraw antialiased lines and polylinesDraw rectangles (outlined and filled)Draw vertical gradientsCopy (and blend) a rectangular segment of the image onto another locationSave it as a PNG fileLoad a PNG file into it (with some restrictions)…all of it in pure Python.
Package: pngcoder
PNGCODER - Obfuscate any dataEncode any file to PNG image or WAV and vise versa, binary safeWarning!When converting to PNG, if the original file ends with one or more NUL control character, it will be removed after decoding backUsage of pngcoder:pngcoder [--decode] [--quiet] [--verbose] input_file output_file

positional arguments:
  input_file     Source file
  output_file    Destination file

optional arguments:
  -h, --help     show this help message and exit
  --quiet, -q    Quiet mode
  --verbose, -v  If passed, a message will be shown for each row of image
                 decoding
  --decode, -d   If passed, source file will be decodedUsage of wavcoder:wavcoder [-h] [--quiet] [--decode] [--samplerate SAMPLERATE] [--channels CHANNELS] input_file output_file

positional arguments:
  input_file            Source file
  output_file           Destination file

optional arguments:
  -h, --help            show this help message and exit
  --quiet, -q           Quiet mode
  --decode, -d          If passed, input file will be decoded from WAV
  --samplerate SAMPLERATE, -s SAMPLERATE
                        Sample rate. Default: 8000
  --channels CHANNELS, -c CHANNELS
                        Number of channels. Default: 1
Package: pngdata
pngdataWrite data to PNG file formatInstallationpython-mpipinstallpngdataCLI UsageEncodepython-mpngdatae<filepath><texttoencode>Decodepython-mpngdatad<filepath>Examplesimportpngdatapngdata.encode('example text','example.png')data=pngdata.decode('example.png')print(data)# prints "example text"# if no fp argument supplied encode returns BytesIOpng_encoded=pngdata.encode('example text')png_decoded=pngdata.decode(png_encoded)print(png_decoded)# prints "example text"# of course you can save BytesIO to filewithopen('bytesio.png','wb')asf:# seek needed, because png_encoded was read beforepng_encoded.seek(0)f.write(png_encoded.read())data=pngdata.decode('bytesio.png')print(data)# prints "example text"
Package: pnglatex
# pnglatexpnglatex, a small program that converts LaTeX snippets to png## Requiremens[Python3](https://www.python.org/downloads/)A LaTeX distribution such as [TeX Live](https://www.tug.org/texlive/) or [MiKTeX](https://miktex.org/)`pdfcrop, pnmtopng, pdftoppm` You should be able to find those programs in your distro package manager or [Homebrew](https://brew.sh/)## InstallIn your terminal, type:```pip install pnglatex```On some systems you might need to use `pip3` instead of `pip`## Usagepnglatex comes with a simple command line interface.To use the cli, you can check the help message using:```pnglatex -h```Here's the help message in full:```usage: pnglatex.py [-h] -c "LaTeX string" [-o filename]pnglatex, a small program that converts latex snippets to pngoptional arguments:-h, --help         show this help message and exit-c "LaTeX string"  The LaTeX string to convert-o filename        The output filename.```Some examples:`pnglatex -c "\[\frac{1}{2}\]"`, `pnglatex -c foo -o foo.png`pnglatex also includes a single function that you can include in your code.Here's its docstring in full:```pythondef pnglatex(tex_string, output=None):"""Produce an png based on a input LaTeX snippet.@param tex_string: The LaTeX string.@param output: The output filename. It can also be a pathlib.Path object.If not provided, this will be randomly generated.@return: A Path object of the output file@raises ValueError: If the input is empty of something went wrong withthe image creation."""```An example usage would be:```pythonfrom pnglatex import pnglatexoutput = pnglatex(r'\[\displaystyle{\sum_{i=0}^{10} 3i}\]', 'output.png')```## LicenceThis program is free software: you can redistribute it and/or modifyit under the terms of the GNU General Public License as published bythe Free Software Foundation, either version 3 of the License, or(at your option) any later version.See [LICENSE](LICENSE) for details.
Package: pngme-api
Python API ClientThis package exposes a synchronous and asynchronous client used to interact with Pngme's financial data APIs.InstallInstall the latest version with:pip3installpngme-apiQuick startCreate aClientinstance using your APItokenfound in thePngme Dashboard:frompngme.apiimportClienttoken=""# your API tokenclient=Client(token)If you're usingasyncio, you can import and use theAsyncClientinstead. Seeusing with asyncio.We can list or search the available/users:users=client.users.get()users=client.users.get(search="2343456789012")For a user of interest, we can get a list of the user's/institutions:user_uuid="33b6215d-3d75-4271-801c-6da27603a8be"institutions=client.institutions.get(user_uuid=user_uuid)Then for a given institution, we can get a list of the user's/transactions,/balances, or/alerts:user_uuid="33b6215d-3d75-4271-801c-6da27603a8be"institution_id="zenithbank"transactions=client.transactions.get(user_uuid=user_uuid,institution_id=institution_id)balances=client.balances.get(user_uuid=user_uuid,institution_id=institution_id)alerts=client.alerts.get(user_uuid=user_uuid,institution_id=institution_id)asyncioWe can make multiple requests concurrently usingasyncioby creating aClientinstance with your APItokenfound in thePngme Dashboard:frompngme.apiimportAsyncClienttoken=""# your API tokenclient=AsyncClient(token)Similar to the synchronousClient, we can list or search the available/users:asyncdefget_users(client:AsyncClient):users=awaitclient.users.get()returnusersusers=asyncio.run(get_users(client))This is helpful to concurrently execute multiple requests, such as fetching a user's transaction history across all accounts by iterating over institutions associated with a user:asyncdefget_transactions(client:AsyncClient,user_uuid:str):# Find institutions where the user has one or more accountsinstitutions=awaitclient.institutions.get(user_uuid)# Concurrently fetch transactions for all institutionsinstitution_ids=[institution.institution_idforinstitutionininstitutions]coroutines=[client.transactions.get(user_uuid,institution_id)forinstitution_idininstitution_ids]transactions=awaitasyncio.gather(*coroutines)# Associate transactions with the relevant institution_idreturndict(zip(institution_ids,transactions))user_uuid="33b6215d-3d75-4271-801c-6da27603a8be"transactions=asyncio.run(get_transactions(client,user_uuid))Next stepsBrowse the PngmeFeature Libraryto see how data scientists integrate our APIs into decisioning workflowsExplore the definitions of each response field in theAPI Docs
Package: pngpadder
# pngpadderA tool for adding extra data to PNG files to pad them out for tests.### Installationpip install pngpadder### Examples:import pngpadder# Pad file size to 100,000 bytes
pngpadder.pad_to(‘/path/to/file.png’, ‘/path/to/output/file.png’, 100000)# Pad file size by 20 bytes
pngpadder.pad_by(‘/path/to/file.png’, ‘/path/to/output/file.png’, 20)Alternatively, from the command line:# Pad file to 100,000 bytes
pngpadder –to 100000 -i /path/to/file.png -o /path/to/output/file.png# Pad file size by 20 bytes
pngpadder –to 100000 -i /path/to/file.png -o /path/to/output/file.png
Package: pngquant
Pngquant is a command-line utility and a library for lossy compression of PNG images.And This Library Is A Python Wrapper Of PngquantInstall Pngquanthttps://pngquant.org/[pngquant 2.0 for ubuntu 12.04 not available](http://askubuntu.com/questions/469171/pngquant-2-0-for-ubuntu-12-04-not-available)Install Python Wrapper Of Pngquantpip install pngquantUsageimport pngquant

pngquant.config('~/pngquant/pngquant')
pngquant.quant_image('~/pngquant/pngquant/pngquant.png')Methoddef config(self, quant_file=None, min_quality=None, max_quality=None, ndeep=None, ndigits=None, tmp_file=None):

def quant_data(self, data=None, dst=None, ndeep=None, ndigits=None, delete=True):

def quant_image(self, image=None, dst=None, ndeep=None, ndigits=None, override=True, delete=True):

def quant_dir(self, dir=None, dst=None, ndeep=None, ndigits=None, override=True, delete=True, topdown=True):
Package: pngquant-cli
pngquant 2pngquantis a PNG compressor that significantly reduces file sizes by converting images to a more efficient 8-bit PNG formatwith alpha channel(often 60-80% smaller than 24/32-bit PNG files). Compressed images are fully standards-compliant and are supported by all web browsers and operating systems.Thisis the officialpngquantrepository. The compression engine is also availableas an embeddable library.Usagebatch conversion of multiple files:pngquant *.pngUnix-style stdin/stdout chaining:… | pngquant - | …To further reduce file size, tryoptipng,ImageOptim, orzopflipng.FeaturesHigh-quality palette generationadvanced quantization algorithm with support for gamma correction and premultiplied alphaunique dithering algorithm that does not add unnecessary noise to the imageConfigurable quality levelautomatically finds required number of colors and can skip images which can't be converted with the desired qualityFast, modern codebased on a portablelibimagequant libraryC99 with no workarounds for legacy systems or compilers (apart from Visual Studio)multicore support (via OpenMP) and Intel SSE optimizationsOptionsSeepngquant -hfor full list.--quality min-maxminandmaxare numbers in range 0 (worst) to 100 (perfect), similar to JPEG. pngquant will use the least amount of colors required to meet or exceed themaxquality. If conversion results in quality below theminquality the image won't be saved (if outputting to stdin, 24-bit original will be output) and pngquant will exit with status code 99.pngquant --quality=65-80 image.png--ext new.pngSet custom extension (suffix) for output filename. By default-or8.pngor-fs8.pngis used. If you use--ext=.png --forceoptions pngquant will overwrite input files in place (use with caution).-o out.pngor--output out.pngWrites converted file to the given path. When this option is used only single input file is allowed.--skip-if-largerDon't write converted files if the conversion isn't worth it.--speed NSpeed/quality trade-off from 1 (slowest, highest quality, smallest files) to 11 (fastest, less consistent quality, light comperssion). The default is 4. It's recommended to keep the default, unless you need to generate images in real time (e.g. map tiles). Higher speeds are fine with 256 colors, but don't handle lower number of colors well.--nofsDisables Floyd-Steinberg dithering.--floyd=0.5Controls level of dithering (0 = none, 1 = full). Note that the=character is required.--posterize bitsReduce precision of the palette by number of bits. Use when the image will be displayed on low-depth screens (e.g. 16-bit displays or compressed textures in ARGB444 format).--stripDon't copy optional PNG chunks. Metadata is always removed on Mac (when using Cocoa reader).Seeman page(man pngquant) for the full list of options.Licensepngquant is dual-licensed:UnderGPL v3or later with an additionalcopyright noticethat must be kept for the older parts of the code.Oracommercial licensefor use in non-GPL software (e.g. closed-source or App Store distribution). You canget the license via Super Source. Emailkornel@pngquant.orgif you have any questions.
Package: pngrenderer
# PNG RendererFor constructing a zip file of png images. I created this package as I had many plots associated from the output of a plotting script and wanted to keep them together. Similar to `matplotlib.backends.PdfPages` I wanted to associate the images, but using the `png` format, so I chose to zip the collection of images. I wanted png images as they contained large amounts of data which created extremely large pdf files.## Example usage``` pythonimport matplotlib.pyplot as pltfrom pngrenderer import PNGRendererfig = plt.figure()plt.plot(...)# Save the resulting image(s) to "out.zip"renderer = PNGRenderer("out")# Save the first png imagerenderer.savefig("first.png")# Alternatively call .save_page# renderer.save_page("first.png")# Render the zip file to diskrenderer.render()# Alternatively use the context managerfrom pngrenderer import png_renderwith png_render("out") as renderer:renderer.savefig()# The context manager calls the `render` method```## Requirements and installationThis package requires matplotlib, but no other packagesInstallation: `pip install git+https://github.com/mindriot101/pngrenderer.git`If this package ever makes it onto pypi then probably the installation will be `pip install matplotlib-pngrenderer`.## ContributingTo contribute, clone the repository at [this github link](https://github.com/mindriot101/matplotlib-pngrenderer), and run the tests with `python setup.py test`
Package: pngrok-jawide
pngrok这是一个使用python实现的内网穿透服务，完全使用标准库实现服务端部署方式pythonserver.py0.0.0.0:400000.0.0.0:4000对外开放40000端口，以供用户访问；开放4000端口，以供客户端访问客户端部署方式pythonclient.py127.0.0.1:4000127.0.0.1:80连接到部署在127.0.0.1上的服务端的4000端口，将部署在127.0.0.1上的本地服务上的流量转发到服务端日志默认不显示日志，使用--log_level选项指定日志级别pythonserver.py0.0.0.0:400000.0.0.0:4000--log_levelINFObuffer size默认为2048，使用--buffer_size选项修改但是目前因为对报文结尾的判断全靠报文大小小于buffer_size的办法判断所以要求server端和client端的buffer_size大小一致将来应该使用自定义报文，或者去解析http报文获取http报文的长度，为了以后考虑应该使用自定义报文pythonserver.py0.0.0.0:400000.0.0.0:4000--buffer_size64开发计划修改报文结尾的判断方法一个服务端可以接入多个客户端，并能对每一个客户端的身份进行认证为服务端建立web服务，让用户可以注册、登录、获取appid, appsecret、下载客户端脚本、创建内网穿透服务
Package: png-splitter
png_splitterSplit a png into smaller images.A simple package that is able to split a full image like a sprite sheet.⚠️This package only works if the background of the png is invisible, otherwise, it will have unexpected results⚠️For example, the following image:Creates 10 smaller images:How to Installpip install png_splitterAll you have to do is:png_splitter.split_image("image.png","images_folder")
Package: pnguin
pnguinA simple, lightweight DataFrame library with dual-axis (either row or column mode) support, and remote DB DataFrames.UsageThis library is currently in developmentWhy pnguin?Well, Penguins are cute. Pandas are cute. This is a Pandas alternative, and the namepenguinwas taken. So I just got rid of thee.
On a fun side note though, this let's us now use this as:import pnguin as pn-- close enough toimport pandas as pd, don't you think?
Package: pniggli
This is a (P)ure python implementation of algorithm to determin Niggli cell.
The library supports both 2D and 3D niggli transformations.Rows of list or rows ofnumpy.ndarraycorrespond basis vectors, a, b, c or a, b
They are input to niggli_reduce as a row with three colum  matrices,
same as most DFT softwares’ lattice inputs.In the implementation details, since the lattice is represented by a row vector,
the transformation operation on the lattice is left-multiplied, such as:importnumpyasnp# TMatrix is the transform matrixnew_Lattice=np.matmul(TMatrix,old_Lattice)For details of the algorithm, see [[Niggli for 2d and 3d]](http://)Install$pipinstallpniggliUsagefrompniggliimportniggli_reduce,niggli_checklattice_3D=[4.912,0.000,0.000,-2.456,4.254,0.000,0.000,0.000,0.000]niggli_lattice=niggli_reduce(lattice_3D)print(niggli_lattice)# Out:# array([[ 4.912,  0.   ,  0.   ],#        [-2.456,  4.254,  0.   ],#        [ 0.   ,  0.   , 16.   ]])print(niggli_check(niggli_lattice))# Truelattice_2D=[2.4560000896,0.0000000000,11.0520002567,2.1269502021]niggli_lattice=niggli_reduce(lattice_2D)print(niggli_lattice)# Out[6]:# array([[-1.2279999 , -2.1269502 ],#        [-1.22800019,  2.1269502 ]])The 2D example is a triangle motif.Versionv0.1.22D and 3D niggli reduce supportniggli_check for 3D latticev0.1.03D niggli reduce supportniggli_check for 3D lattice
Package: pninexus
Python wrapper for theh5cppandlibpninexusC++
libraries. The wrapper supports Python 2.X and 3.X.InstallationRequired packagesh5cpp>= 0.5.0libpninexus>= 3.2.0python setuptoolsnumpyc++ compilerboost-python librarypython sphinx to build the documentationInstall from sourcesThe code can be built with$ python setup.py installFor those who are still running on the old interface it is maybe whise
to install this package in a custom location with something like this$ python setup.py install --prefix=<path to installation prefix>To build the documentation use$ python setup.py build_sphinxThe resulting documentation can be found belowbuild/sphinx/htmlin
the root directory of the source distribution.Finally, the package can be tested using$ python setup.py testFor Python3 just replace python with python3 in the above instructions.Debian and Ubuntu packagesDebianbookworm,bullseye,busteror Ubuntulunar,jammy,focalpackages can be found in the HDRI repository.To install the debian packages, add the PGP repository key$ sudo su
$ curl -s http://repos.pni-hdri.de/debian_repo.pub.gpg  | gpg --no-default-keyring --keyring gnupg-ring:/etc/apt/trusted.gpg.d/debian-hdri-repo.gpg --import
$ chmod 644 /etc/apt/trusted.gpg.d/debian-hdri-repo.gpgand then download the corresponding source list, e.g. forbullseye$ cd /etc/apt/sources.list.d
$ wget http://repos.pni-hdri.de/bullseye-pni-hdri.listorjammy$ cd /etc/apt/sources.list.d
$ wget http://repos.pni-hdri.de/jammy-pni-hdri.listrespectively.Finally,$ apt-get update
$ apt-get install python-pninexusor$ apt-get update
$ apt-get install python3-pninexusfor python3.Manylinux wheels from PyPITo install pninexus in a conda or python virtual environment manylinux pip wheels can be used, e.g.$python3-mvenvmyvenv$.myvenv/bin/activate$pipinstallpip--upgrade$pipinstallnumpy$pipinstallpninexus-UwheelFor some versions of wheels numpy needs to be installed in advance.Starting from v3.2.2 the pninexus wheel contains the most important external filters.
In order to use them one needs to set HDF5_PLUGIN_PATH e.g. byimportpninexusMore information can be found atonline
documentation.Changes for a specific version of libpninexus can be found atCHANGELOG.