Saravanan666/pypi-org huggingface dataset:
Package: undouble
undoubleThe aim ofundoubleis to detect (near-)identical images. It works using a multi-step process of pre-processing the images (grayscaling, normalizing, and scaling), computing the image hash, and the grouping of images. A threshold of 0 will group images with an identical image hash. The results can easily be explored by the plotting
functionality and images can be moved with the move functionality. When moving images, the image in the group with the largest resolution will be copied, and all other images are moved to theundoublesubdirectory. In case you want to cluster your images, I would recommend reading theblogand use theclustimage library.The following steps are taken in theundoublelibrary:Read recursively all images from directory with the specified extensions.Compute image hash.Group similar images.Move if desired.⭐️ Star this repo if you like it ⭐️BlogsRead the blog to get a structured overview how todetect duplicate images using image hash functions.Documentation pagesOn thedocumentation pagesyou can find detailed information about the working of theundoublewith many examples.InstallationIt is advisable to create a new environment (e.g. with Conda).condacreate-nenv_undoublepython=3.8
condaactivateenv_undoubleInstall bnlearn from PyPIpipinstallundouble# new installpipinstall-Uundouble# update to latest versionDirectly install from github sourcepipinstallgit+https://github.com/erdogant/undoubleImport Undouble packagefromundoubleimportUndoubleExamples:Example: Grouping similar images of the flower datasetExample: List all file names that are identificalExample: Moving similar images in the flower dataset# -------------------------------------------------# >You are at the point of physically moving files.# -------------------------------------------------# >[7] similar images are detected over [3] groups.# >[4] images will be moved to the [undouble] subdirectory.# >[3] images will be copied to the [undouble] subdirectory.# >[C]ontinue moving all files.# >[W]ait in each directory.# >[Q]uit# >Answer: wExample: Plot the image hashesExample: Three different importsThe input can be the following three types:* Path to directory
* List of file locations
* Numpy array containing imagesExample: Finding identical mnist digitsCitationPlease cite in your publications if this is useful for your research (see citation).MaintainersErdogan Taskesen, github:erdogantContributeAll kinds of contributions are welcome!If you wish to buy me aCoffeefor this work, it is very appreciated :)LicenceSeeLICENSEfor details.Other interesting stufhttps://github.com/JohannesBuchner/imagehashhttps://towardsdatascience.com/a-step-by-step-guide-for-clustering-images-4b45f9906128
Package: undrdg
UNDRDG provides building blocks to convert existing datasets to the UNDR format.The scripts used to convert the datasets that we re-distribute can be found inscripts. The converted datasets can be browsed athttps://www.undr.space.Use UNDRDG in your own projectpython3-mpipinstallundrdgSeescriptsfor usage examples.Add scripts to this directorypython3-mvenv.venvsource.venv/bin/activate
pipinstall-rscripts/requirements.txtContribute to UNDRDGInstall the development versionpython3-mvenv.venvsource.venv/bin/activate
pipremoveundrdg# to avoid name clashespipinstall-e.Edit files inundrg. Changes are automatically reflected in scripts that import the library.Format and lint.isort.;black.;pyright.
Package: undrp
ABOUTundrpconverts Barnes & Noble’s proprietary fixed-layout EPUB files (akaBNFixedFormator DRP) to more generic formats such as
PDF. The goal is to allow you to read these e-books in non-Barnes & Noble e-readers.INSTALLATIONundrprequires Python 3.3 or later. You can install it via pip:pip install undrpUSAGEYou can runundrplike the UNIXcpcommand.Convert a single file (explicit target filename):undrp /path/to/somebook.epub somebook.pdfConvert a single file (implicit target filename):undrp /path/to/somebook.epub .Convert multiple files:undrp /path/to/*.epub .Convert a single file, outputting to stdout:undrp /path/to/somebook.epub -LIMITATIONSundrpis still in its infancy, so there are quite a few limitations:Can only handle EPUB files where all the pages are JPEG images.Error reporting is terrible.Cannot convert EPUB files with DRM. You will need to strip the DRM off first.Can only output to PDF format.
Package: unduplicate-itunes
unduplicate-itunesMoves duplicate song files from your iTunes music directory to the trash.iTunes doesn't automatically remove missing songs, so after you use this package to delete them,follow these instructions.Depending on the structure of your iTunes music directory, it's possible that some duplicate files will be untouched while their original counterparts will be deleted instead (e.g. song.mp4 is removed, song 1.mp4 survives). Although, duplicates and originals look identical in iTunes regardless so this problem only matters if you care about the file names.BehaviorYou are prompted to make a backup of your iTunes music directory before executing. Even if you don't, you can still restore your files from the trash after the script is finished.You are prompted with the number of duplicates to be removed before executing.Directories (usually albums folders) that only contain duplicate files will be moved to the trash.A log file of all actions is output once the script finishes. It is created in whatevercdyou have your terminal set to.Install (PyPi)pip install unduplicate-itunesUsageUsingremove_by_artist_and_title.pycdinto any directory (the log file will be dumped here)python
from unduplicate_itunes import remove_by_artist_and_title as undup
undup.main()When you are prompted to enter the iTunes music directory, you can find the path by right clicking on any song in iTunes and selectShow in Windows ExplorerorShow in Finder(macOS). Grab the root folder that contains all the artist folders.There are other scripts included that try to find duplicates by other methods but they are unfinished and aren't as effective at finding all duplicates.Cleanup iTunes songsOnce the files are trashed, you can remove the dead songs from iTunes usingthese instructions here.
Package: unearth
unearthA utility to fetch and download python packagesNOTICEThis project is still in its early stage and the API may change before 1.0 release.Why this project?This project exists as the last piece to complete the puzzle of a package manager. The other pieces are:resolvelib- Resolves concrete dependencies from a set of (abstract) requirements.unearth(This project)- Finds and downloads the best match(es) for a given requirement.build- Builds wheels from the source code.installer- Installs packages from wheels.They provide all the low-level functionalities that are needed to resolve and install packages.Why not pip?The core functionality is basically extracted from pip. However, pip is not designed to be used as a library and hence the API is not very stable.
Unearth serves as a stable replacement for pip'sPackageFinderAPI. It will follow the conventions ofSemantic Versioningso that downstream projects can use it to develop their own package finding and downloading.Requirementsunearth requires Python >=3.8Installation$python-mpipinstall--upgradeunearthQuickstartGet the best matching candidate for a requirement:>>>fromunearthimportPackageFinder>>>finder=PackageFinder(index_urls=["https://pypi.org/simple/"])>>>result=finder.find_best_match("flask>=2")>>>result.bestPackage(name='flask',version='2.1.2')Using the CLI:$unearth"flask>=2"{"name":"flask","version":"3.0.0","link":{"url":"https://files.pythonhosted.org/packages/36/42/015c23096649b908c809c69388a805a571a3bea44362fe87e33fc3afa01f/flask-3.0.0-py3-none-any.whl","comes_from":"https://pypi.org/simple/flask/","yank_reason":null,"requires_python":">=3.8","metadata":"https://files.pythonhosted.org/packages/36/42/015c23096649b908c809c69388a805a571a3bea44362fe87e33fc3afa01f/flask-3.0.0-py3-none-any.whl.metadata"}}DocumentationRead the docs
Package: unearthed-cli
Crowd ML CLIThis is the command line tool forUnearthed Solutions CrowdML challenges.It is designed to make model validation and submission straightforward.Please see theUnearthed Solutions Terms and ConditionsCommandsunearthed loginPrompts the user for their innovator portal login and stores the tokens. You must be logged in to run other commands.unearthed newThis command asks the user which challenge they wish to download and unpack the model template for. The template will be unpacked in a named folder inside the current directory. A custom path can be specified withunearthed new /path/to/dir/unearthed submitThis will bundle the source code in a Docker container and then submit the code to the submission pipeline. The user is provided a link to the submission tracker, which will auto-popup in their browser, so they can track the progress and logs of their submission.You can disable the tracker from opening in the browser by specifyingunearthed submit --no-trackerunearthed trackerWill open the tracker for the last submission that was made.unearthed preprocessThis command will run the preprocess code in a Docker container similar to how it will execute in the submission pipeline. It can be used to validate the preprocessing code is working before submitting.unearthed trainThis command will train a model, including calling the preprocessing code, in a Docker container similar to how it will execute in the submission pipeline. It can be used to validate the training code is working before submitting.unearthed predictThis command will simulate generating predictions on the public dataset so that the predictions can be inspected locally before submitting.unearhted scoreThis command will run the scoring function to generate a score against the public dataset. It can be used to check the score against the public dataset before submitting.
Package: uneedtest
For historical reasons, Python's built-inunittestmodule is not PEP-8
compliant. It uses camel case method names instead of snake case names. This
is not going to change in Python 3. (1,2,3)This could lead to stylistic inconsistencies, especially if one wants to write
custom assertion methods and runs a pep-8 checker over their test code.To address this stylistic itch,uneedtestoffers a simple drop-inTestCaseclass which provides snake cased aliases to all the camel cased
methods inunittest.TestCase.Installpip install uneedtestUseYou can useuneedtest.TestCasein the same way you used to useunittest.TestCasebefore. It accepts calls to the corresponding snake case
methods. The camel case methods are still working, allowing for gradually fading
out their use in your test code base:fromuneedtestimportTestCaseclassTestMe(TestCase):deftest_something(self):self.assert_equal(1,1)self.assertEqual(1,1)TestYou can run the tests usingpoetry:poetry run pytestIf you don't want to usepoetry, you can run the tests withpytestorunittestdirectly:pytest tests/
Package: unequalpy
unequalPy: A package for the unequal-time matter power spectrumThis package contains functions to obtain the unequal-time power spectrum at one-loop
in standard perturbation theory an effective field theory. It also provides functions
to reproduce the analysis in[1]:Approximations to the unequal-time matter power spectrum: geometric and the midpoint approximations.Weak lensing functions: filters and lensing efficiency.Tests using DES-Y1 data and cosmoSIS.The full list of features can be found in theunequalPy Documentation.If you use UnequalPy for work or research presented in a publication please follow
ourCitation Guidelines.Getting StartedunequalPy is distributed throughPyPI. To install UnequalPy and itsdependenciesusingpip:$pipinstallunequalpyThe unequalPy library can then be imported from python:>>>importunequalpy>>>help(unequalpy)References[1]de la Bella, L. and Tessore, N. and Bridle, S., 2020. Unequal-time matter power spectrum: impact on weak lensing observables.arXiv 2011.06185.
Package: unet
PyTorch implementation of 2D and 3D U-Net.The U-Net architecture was first described inRonneberger et al. 2015, U-Net: Convolutional Networks for Biomedical Image
Segmentation.
The 3D version was described inÇiçek et al. 2016, 3D U-Net: Learning Dense Volumetric Segmentation from
Sparse Annotation.Installationpip install unetCreditsIf you used this code for your research, please cite this repository using the
information available on itsZenodo entry.History0.4.0 (2019-10-29)First release on PyPI.
Package: unetpy
This python packageunetpyprovides UnetSocket APIs to interact with any modems running UnetStack. Theunetpypackage is built uponfjagepy. This package allows the developers and users to interact with the modem using an interface implementd in python. All the requests made to the modem are using JSON messages. The relevant JSON messages are constructed and sent over some low level networked connection like TCP. The UnetStack server running on the modem understands these messages, takes corresponding actions and returns the notifications and/or responses back in the form of JSON messages which are parsed byunetpy.UsageInstallation:pip install unetpyTo import all general modules:from unetpy import *Sample notebook:python-gateway-tutorial.ipynbUseful linksunetpy homeUnet project home
Package: unet-torch
UnetMy implemenetation of a modular Unet from the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation"Paper LinkAppreciationLucidrainsAgoriansInstallpip install unetLicenseMITCitations@misc{1505.04597,Author={Olaf Ronneberger and Philipp Fischer and Thomas Brox},Title={U-Net: Convolutional Networks for Biomedical Image Segmentation},Year={2015},Eprint={arXiv:1505.04597},}
Package: unexceptional
UnexceptionalTired of all those pesky uncaught Exceptions?pip install unexceptionalIn the REPL,In the CLI,In IPython & Jupyter,No exceptions.How do I escape this fresh hell?pip uninstall unexceptionalWhat is even happening?In order to achieve transparency to the user, this package is invoked at every interpreter startup by means of an included .pth file.For the CPython interpreter,sys.excepthookis overridden with a no-op.For IPython, a crime is committed:Because the .pth file is invoked before IPython, a bastardizedimportlib.abc.MetaPathFinderis inserted intosys.meta_pathto intercept future imports ofIPython.core.interactiveshell.
TheInteractiveShell.showtracebackmethod is then overridden with the no-op after the module is executed.
Package: unexpectation-handler
Unexpectation HandlerNever trust users! Encapsulate "isolating program vulnerabilities (errors, exceptions, or results due to unexpected input) that may be caused by executed code" and "calling function that handle them" as a package of Python.⚖️LicenseMIT📗DocumentationThe documentation is hosted on Github repository:https://github.com/leoweyr/Python-Unexpectation_Handler/blob/main/doc
Package: unexpected-isaves
Unexpected image savesGenerate images as a spreadsheet, a Minecraft pixel art, an ascii art, or a rubik's cube art using PythonA quick scroll through social media and you'll find very talented people making portraits out of dice, rubik's cube, in minecraft as pixel art, or even using MIDI notes on a Digital Audio Workstation (DAW). This package gives you the ability to do the same, with or without talent.
Currently it can only generate arts using minecraft, the cells of a spreadsheet, ascii characters, or rubik's cubes, but if you have any ideas that fit into the project's goal pleaselet me know, or - even better - submit a merge request with your work!Table of contentsWhat does this package do :thinking:;How to use it :computer:;Why unexpected-isaves?;Contributing :pencil:;Into the wildWhat does this package do :thinking:With this package you can save anyimageon a givenpathas aSpreadsheet:Minecraft Pixel Art Datapack:Ascii Art:Rubik's Cube:How to use it :computer:First you install it in your environment like thispipinstallunexpected-isavesThen you can start using it already! Pass the path of any image you've got locally and try to save it using our functions!fromunexpected_isavesimportsave_imagesave_image.to_excel(image="my_image.png",path="/home/user/Documents/my_image.xlsx")Why unexpected-isaves?You might be wondering: why would I ever need such a useless lib? The answer is: you wouldn't. This lib was created for learning purposes, and it was never my intention to make it useful. It might be a nice way to impress your friends on your Minecraft server, or to make an important presentation lighter with a fun spreadsheet art, though. Be creative!You can read ourwikifor more information about this project if you want.Contributing :pencil:Just like Minecraft, everything's better when shared and built together.Contributions are welcome and appreciated. Make sure to read ourguide for contributingand don't forget to check out ourcode of conduct.Into the wildSo you've used this tool to create the most awesome art ever? Please consider leaving a star on the repo. It helps a lot.Have fun!
Package: unf
python-unfunf contains Python code for Universal Numeric Fingerprints (UNFs).UNFs are defined athttp://guides.dataverse.org/en/latest/developers/unf/index.html.Usage:>>> import unf
>>> unf.unf(1.23456789)
'UNF:6:vcKELUSS4s4k1snF4OTB9A=='
>>> unf.unf(1.23456789, digits=9)
'UNF:6:N9:IKw+l4ywdwsJeDze8dplJA=='Please see ROUNDING.md for a note about rounding.
Package: unfairseq
For fairseq softwareCopyright (c) 2017-present, Facebook, Inc. All rights reserved.Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:Redistributions of source code must retain the above copyright notice, thislist of conditions and the following disclaimer.Redistributions in binary form must reproduce the above copyright notice,this list of conditions and the following disclaimer in the documentationand/or other materials provided with the distribution.Neither the name Facebook nor the names of its contributors may be used toendorse or promote products derived from this software without specificprior written permission.THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.Description: Unofficial Pacakge of fairseq
Platform: UNKNOWN
Package: unfancy
Unfancy textThis program converts "fancy text", that is, text written with an alternative
set of Latin characters, such asmathematical alphabetto plain
Latin characters.UsageUse as commandline interface:$ python unfancy.py
Press Ctrl+C or Ctrl+D to exit
𝐓𝐡𝐞 𝐪𝐮𝐢𝐜𝐤 𝐛𝐫𝐨𝐰𝐧 𝐟𝐨𝐱 𝐣𝐮𝐦𝐩𝐬 𝐨𝐯𝐞𝐫 𝐭𝐡𝐞 𝐥𝐚𝐳𝐲 𝐝𝐨𝐠
Fancy type: mathematical bold
The quick brown fox jumps over the lazy dogUse as a libraryfromunfancyimportcreate_multi_alphabet_map,translate_textalphabet_map=create_multi_alphabet_map()# alphabet_map parameter is optional,# but it is recommended to create it separately to avoid repetitionfancy_text="𝐓𝐡𝐞 𝐪𝐮𝐢𝐜𝐤 𝐛𝐫𝐨𝐰𝐧 𝐟𝐨𝐱 𝐣𝐮𝐦𝐩𝐬 𝐨𝐯𝐞𝐫 𝐭𝐡𝐞 𝐥𝐚𝐳𝐲 𝐝𝐨𝐠"unfancy_text,label=translate_text(fancy_text,alphabet_map)To doSupport Greek mathematical alphabetsAllow select specific character sets to make translationPackage the thing
Package: unflatten
DescriptionThis package provides a function which can unpack a flat dictionary
into a structureddictwith nested sub-dicts and/or sub-lists.Development takes place ongithub.
The package is installable fromPyPISynopsisNested dicts:>>> from unflatten import unflatten

>>> unflatten({'foo.bar': 'val'})
{'foo': {'bar': 'val'}}Nested list:>>> unflatten({'foo[0]': 'x', 'foo[1]': 'y'})
{'foo': ['x', 'y']}Nested lists and dicts, intermixed:>>> unflatten({
...     'foo[0][0]': 'a',
...     'foo[0][1]': 'b',
...     'foo[1].x': 'c',
...      })
{'foo': [['a', 'b'], {'x': 'c'}]}NotesUnflattentake a single argument which should either be adict(or an object with a dict-like.items()or.iteritems()method) or a sequence of(key, value)pairs.
All keys in the dict or sequence must be strings.
(Under python 2, keys must be instances ofbasestring; under
python 3, keys just be instances ofstr.)Unflattenalways returns adict.  By way of example:>>> unflatten([('[0]', 'x')])
{'': ['x']}For list-valued nodes, all indexes must be present in the input
(flattened) mapping, otherwise aValueErrorwill be thrown:>>> unflatten({'a[0]': 'x', 'a[2]': 'y'})
Traceback (most recent call last):
...
ValueError: missing key 'a[1]'See AlsoThemorphandflatterypackages purport to implement similar functions.AuthorsJeff DairikiHistoryRelease 0.1.1 (2021-08-16)NitsFix backslashes in docstringsPackagingPEP517-ize the packagingUsesetuptools-scmto maintain version numbersTestingTest under Python 3.7, 3.8, 3.9 and PyPy 3.7. Drop testing for Python 3.4 & 3.5.Pin pip version for pypy2 (seepip #8653)Clean up and modernize the toxlintandcoverageenvironmentsRelease 0.1 (2018-01-17)No code changes.This package is now deemed “production ready” (though your mileage may vary.)Release 0.1b1 (2018-01-09)Initial release.
Package: unfold-protein
Unfold-proteinUnfold-protein is a set of tools for controlling velocity-clamp single
molecule force spectroscopy.  It uses thepyafmpackage for low level
AFM control.  This package contains the high-level experiment control
logic.PackagesGentooI’ve packaged pyafm for Gentoo.  You needlaymanand mywtk
overlay.  Install with:# emerge -av app-portage/layman
# layman --add wtk
# emerge -av sci-physics/unfold-proteinDependenciesUnfold-protein requires the following Python modules:PyafmPypiezo(required directly, and viapyafm)H5config(required directly, and viapyafm)h5py(required directly, and viah5config)MatplotlibNumPyGetting the sourceUnfold-protein is available as aGitrepository:$ git clone git://tremily.us/unfold-protein.gitThere are also periodic bundled releases.  For example, get version
0.2 as a gzipped tarball with:$ wget 'http://git.tremily.us/?p=unfold-protein.git;a=snapshot;h=v0.2;sf=tgz'
$ tar -xzvf unfold-protein-0.2.tar.gzInstallationAfter downloading, change to the source directory and run:$ python setup.py installto install unfold-protein.  Run:$ python setup.py install --helpto see a list of installation options you may want to configure.UsageTheunfold.pyscript runs a series of unfolding pulls while
scanning the pulling velocity and contact position.  It has a few
command line options; get details with:$ unfold.py --helpYou can configure the unfolding and scanning behavior usingh5config.
The configuration is stored in~/.config/unfold-default.yaml.  To
seed this configuration file before tweaking it, you should configurepyafm(as described in itsREADME).  Then run:>>> import unfold_protein.storage as storage
>>> config = storage.get_default_config()
>>> storage.save_scan_config(config=config)to create a configuration using the default settings.  TheYAMLsyntax is plain text, which you can edit as you see fit.  Future runs
ofunfold.py(and calls tounfold_protein.storage.load_scanner()) will load this
configuration by default.unfold.pysaves each unfolding pull in its own timestamped file
with the unfolding data along with the complete configuration used to
acquire it.  You can configure the directory where these files are
stored with theunfold/save/base directorysetting.  You can
convert the saved unfolding data toPNGswithplot-unfold.py.
For example:$ plot-unfold.py ~/rsrch/data/unfold/*.h5For more detailed analysis, you may want to useHooke.  You may also
want to usecalibcantto calibrate your AFM cantilever’s bending
spring constant.