Saravanan666/pypi-org huggingface dataset:
Package: pyphashml
Python package to compute image perceptual hashes.  The
perceptual hash is based on the mobilenetv2 tensorflow image
classification model.  It is condensed down to 32-bytes per
image.  Distance between two hashes gives some measure of image
similarity, where distance(a,b) == 0 means idential images and
similar images give lower distance.UsageRun unit testspython -m unittest tests.test_phashmlBuild the packagecd pyPhashML
python -m build .Install binarypip install pyphashml-0.0.1-py3-none-any.whlCalculate perceptual hash for two images and calculate distance:from pyphashml.phashml import phashmlctx

x = phashmlctx.imghash("/path/to/imgfile.jpg")
y = phashmlctx.imghash("/path/to/imgfile2.jpg")
d = phashmlctx.hamming_distance(x, y)x,y are bitstring objects.  d is an integer value >= 0.Submit perceptual hashes for a directory of images to ImageScoutPro serverpython -m pyphashml.imgscoutclient --dir /path/to/img/files --key mykey --host 127.0.0.1 --port 6379 --db 0Compare two images by their perceptual hashespython3 -m pyphashml.imgcmp /path/to/img/file.jpg /path/to/img/file2.jpgReqiresbitstring
numpy
tensorflow >=1.14,<2
redis (for the imgscoutclient command to submit to imgscout server)
Package: pyphaxio
A Python module for interacting with thePhaxio API.InstallationVia pip:$ pip install pyphaxioUsageSend a fax to multiple people using HTML message:fromphaxioimportPhaxioApiapi=PhaxioApi(key,secret)r=api.send(to=['4141234567','5141234567','6151234567'],string_data='Hello World!',string_data_type='text')print(r.get('faxId'))Generally, each supported method takes keyword arguments with the exact
same names of the API method parameters as they’re described in thedocumentation. One exception to this rule is thatfilenameis
instead referred to asfiles. For example, to send a fax using
files:llama=os.path.join(os.path.dirname(__file__),'tests/llama.pdf')alpaca=os.path.join(os.path.dirname(__file__),'tests/alpaca.pdf')f=open(alpaca,'rb')r=api.send(to='4141234567',files=(llama,f))See thetestsfor additional examples.Error HandlingBy default, the api calls return a dictionary. However, you can usePhaxioApi(key, secret, raise_errors=True)which will raise the
following errors: *AuthenticationError- key/secret are invalid *APIError- error with api call *ServerError- server had an
error and could not complete your requestErrors can be imported from thephaxio.exceptionsmodule.Currently Supported API CallsImplemented and tested:sendtestReceivefaxStatusfaxFileImplemented and untested:attachPhaxCodeToPdfcreatePhaxCodegetHostedDocumentprovisionNumberreleaseNumbernumberListfaxListfaxCancelaccountStatusTestingpython setup.py testYou will be prompted for a test api key and secretContributingFork itCreate your feature branch (git checkout-bmy-new-feature)Commit your changes (git commit-am'Added some feature')Push to the branch (git push originmy-new-feature)Create new Pull Request
Package: pyphen
Pyphen is a pure Python module to hyphenate text using existing Hunspell
hyphenation dictionaries.This module is a fork of python-hyphenator, written by Wilbert Berendsen.Many dictionaries are included in pyphen, they come from the LibreOffice git
repository and are distributed under GPL, LGPL and/or MPL. Dictionaries are not
modified in this repository. See the dictionaries and LibreOffice’s repository
for more details.https://cgit.freedesktop.org/libreoffice/dictionaries/tree/Free software: GPL 2.0+/LGPL 2.1+/MPL 1.1 tri-licenseFor Python 3.7+, tested on CPython and PyPyDocumentation:https://doc.courtbouillon.org/pyphenChangelog:https://github.com/Kozea/pyphen/releasesCode, issues, tests:https://github.com/Kozea/pyphenCode of conduct:https://www.courtbouillon.org/code-of-conductProfessional support:https://www.courtbouillon.orgDonation:https://opencollective.com/courtbouillonPyphen has been created and developed by Kozea (https://kozea.fr).
Professional support, maintenance and community management is provided by
CourtBouillon (https://www.courtbouillon.org).Copyrights are retained by their contributors, no copyright assignment is
required to contribute to Pyphen. Unless explicitly stated otherwise, any
contribution intentionally submitted for inclusion is licensed under
GPL 2.0+/LGPL 2.1+/MPL 1.1, without any additional terms or conditions. For
full authorship information, see the version control history.
Package: pyPhenology
pyPhenologypyPheonology is a software package for building plant phenology models. It has
numpy at it’s core, making model building and prediction extremely fast. The
core code was written to model phenology observations from the National
Phenology Network, where abundant species have several thousand observations.
The API is inspired by scikit-learn, so all models can work interchangeably
with the same code. pyPhenology is currently used to build the continental
scale phenology forecasts onhttp://phenology.naturecast.orgFull documentationhttp://pyphenology.readthedocs.io/en/master/InstallationRequires: scipy, pandas, joblib, and numpyInstall via pippip install pyPhenologyOr install the latest version from Githubpip install git+git://github.com/sdtaylor/pyPhenologyGet in touchSee theGitHub Repoto see the
source code or submit issues and feature requests.CitationIf you use this software in your research please cite it as:Taylor, S. D. (2018). pyPhenology: A python framework for plant phenology
modelling. Journal of Open Source Software, 3(28), 827.https://doi.org/10.21105/joss.00827Bibtex:@article{Taylor2018,
author = {Taylor, Shawn David},
doi = {10.21105/joss.00827},
journal = {Journal of Open Source Software},
mendeley-groups = {Software/Data},
month = {aug},
number = {28},
pages = {827},
title = {{pyPhenology: A python framework for plant phenology modelling}},
url = {http://joss.theoj.org/papers/10.21105/joss.00827},
volume = {3},
year = {2018}
}AcknowledgmentsDevelopment of this software was funded bythe Gordon and Betty Moore Foundation's Data-Driven Discovery InitiativethroughGrant GBMF4563to Ethan P. White.
Package: pyphenomd
pyphenomd - a python implementation of PhenomD waveformspyphenomd is a tool designed to support the BOWIE package. The paper detailing this tool and examples of its usage can be found at arXiv:1807.02511 (Evaluating Black Hole Detectability with LISA).  This piece of the package is a waveform generator for general use (pyphenomd.pyphenomd). The waveform generator creates PhenomD waveforms for binary black hole inspiral, merger, and ringdown. PhenomD is from Husa et al 2016 (arXiv:1508.07250) and Khan et al 2016 (arXiv:1508.07253). Please refer to these papers for information on the waveform construction.pyphenomd also includes a fast signal-to-noise ratio calculator for these waveforms based on stock or input sensitivity curves. The package also includes a code to read out the sensitivity curves from the text files provided.For usage of this tool, please cite all three papers mentioned above (arXiv:1807.02511, arXiv:1508.07250, arXiv:1508.07253).See pyphenomd_guide.ipynb for more information and examples.See BOWIE documentation, paper, and examples for more information ways to use pyphenomd.Getting StartedThese instructions will get you a copy of the project up and running on your local machine for usage and testing purposes.PrerequisitesSoftware installation/usage only requires a few specific libraries in python. All libraries are included with Anaconda. If you do not run python in an anaconda environment, you  will need the following libraries and modules to run with all capabilities: Numpy, Scipy, and astropy. All can be installed with pip. For example, within your python environment of choice:pip install astropyIn order to properly create waveforms with ctypes, you will need complex, gsl, and math c libraries. For installing gsl, refer tohttps://www.gnu.org/software/gsl/or install it through anaconda.Installingpip install pyphenomdThis will download the all necessary parts of the package to your current environment. It will not download the notebooks for testing and example usage.Testing and Running an ExampleTo test the codes, you run the guide notebook.jupyter notebook pyphenomd_guide.ipynbContributingPlease readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull requests to us.VersioningCurrent version is 1.0.1.We useSemVerfor versioning.AuthorsMichael Katz-mikekatz04Please email the author with any bugs or requests.LicenseThis project is licensed under the GNU License - see theLICENSE.mdfile for details.AcknowledgmentsThanks to Michael Puerrer, Sebastian Khan, Frank Ohme, Ofek Birnholtz, Lionel London for authorship of the original c code for PhenomD within LALsuite.
Package: pyphenopop
Mixed Cell Population Identification PackageThis package contains methods designed to determine the existance of
subpopulations with different responses to a given drug from a dataset of
screenings with that drug on the total cell population. The implementation is
based on the method presented in the article"Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data".
The dataset should contatin cell viability data tested on C different drug concentrations over
N time points and containing R replicates. The package can then estimate the
number of subpopulations, their mixture proportions and a dose-reponse curve
for each of the subpopulations. A Matlab package of the method is available athttps://github.com/ocbe-uio/PhenoPop.InstallThe package can be easily install viapip install pyphenopop. You can also install it from theGithub repositoryusingpip install git+https://github.com/ocbe-uio/pyPhenoPop.gitor by cloning the repositorygit clone https://github.com/ocbe-uio/pyPhenoPopand installing from the local repository viapip install .UsageA tutorial using data from the originalpublicationis provided inexamples/tutorial.ipynb. Additional information can be obtained by executingfrom pyphenopop.mixpopid import mixture_id
help(mixture_id)PublicationWhen using pyPhenoPop in your project, please citeKöhn-Luque, A., Myklebust, E. M., Tadele, D. S., Giliberto, M., Schmiester, L., Noory, J., ... & Foo, J. (2023). Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data. Cell Reports Methods, 3(3).@article{kohn2023phenotypic,
  title={Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data},
  author = {Alvaro Köhn-Luque and Even Moa Myklebust and Dagim Shiferaw Tadele and Mariaserena Giliberto and Leonard Schmiester and Jasmine Noory and Elise Harivel and Polina Arsenteva and Shannon M. Mumenthaler and Fredrik Schjesvold and Kjetil Taskén and Jorrit M. Enserink and Kevin Leder and Arnoldo Frigessi and Jasmine Foo},
  journal={Cell Reports Methods},
  volume={3},
  number={3},
  year={2023},
  doi={https://doi.org/10.1016/j.crmeth.2023.100417},
  publisher={Elsevier}
}
Package: pypher
Compute an homogenization kernel between two PSFs.This code is well suited for PSF matching applications in both an astronomical or microscopy context.It has been developed as part of the ESAEuclidmission and is currently being used for multi-band photometric studies ofHST(visible) andHerschel(IR) data.Paper:http://arxiv.org/abs/1609.02006Documentation:https://pypher.readthedocs.ioFeaturesWarp(rotation + resampling) the PSF images (if necessary),Filterimages in Fourier space using a regularized Wiener filter,Producea homogenization kernel.Note:pypherneeds the pixel scale information to be present in the FITS files. If not, use the providedaddpixsclmethod to add this missing info.Warning:This codedoes notinterpolate NaN values (replaced by 0 instead),center PSF images,minimize the kernel size.InstallationPyPHER works both with Python 2.7 and 3.4 or above and relies onnumpy,scipyandastropylibraries.Option 1:PippipinstallpypherOption 2: fromsourcegitclonehttps://github.com/aboucaud/pyphercdpypherpythonsetup.pyinstallOption 3: fromconda-forgecondainstall-cconda-forgepypherBasic example$pypherpsf_a.fitspsf_b.fitskernel_a_to_b.fits-r1.e-5This will create the desired kernelkernel_a_to_b.fitsand a short
logkernel_a_to_b.logwith information about the processing.AcknowledgingIf you make use of any product of this code in a scientific publication,
please consider acknowledging the work by citing the paper using the BibTeX
information in theCite this repositorysection at the top right of the page.
Package: pyphetools
pyphetoolsPython Phenopacket ToolsThis package currently is designed to test how to use the Python version of the phenopackets package together with pandas to create phenopackets from typical supplemental tables.Development goals include making builder code similar to the Java package to create valid phenopackets and to perform JSON Schema-based validation.DocumentationSee the tutorial and detailed documentationhere.PyPIWe have made the package available in the Python Package Index (pypi) atpyphetools.
Package: pyphishtanklookup
PyPhishtank LookupThis is the client API forPhishtank Lookup,
not the official phishtank API.InstallationpipinstallpyphishtanklookupUsageCommand lineYou can use thephishtank-lookupcommand to search in the database:usage:phishtank-lookup[-h][--urlURL](--info|--url_queryurl|--urls_by_cccc|--urls_by_ipip|--urls_by_asnasn)SearchaURLinPhishtankLookup.

optionalarguments:-h,--helpshowthishelpmessageandexit--urlURLURLoftheinstance(defaultstohttps://phishtankapi.circl.lu/).--infoInfoavouttheinstance.--url_queryurlURLtosearch.--urls_by_ccccCountryCodetosearch.--urls_by_ipipIPaddresstosearch.--urls_by_asnasnASNtosearch.LibrarySeeAPI Reference
Package: pyphm
Machinery data, made easyDatasets specific to PHM (prognostics and health management). Use Python to easily download and prepare the data, before feature engineering or model training.Current datasets:UC-Berkeley Milling Dataset:example notebook(open in Colab);dataset sourceIMS Bearing Dataset:dataset sourceAirbus Helicopter Accelerometer Dataset:dataset sourceMore coming soon!Alpha NoticePyPHM is in active development. Expect considerable changes in the near future.Our goals are to create:A package that implementscommon data preprocessing methodsused by others.A package with acoherent and thoughtful API.Thoroughdocumentation, with plenty ofexamples.A package that is welltested, with the use oftype hints.A package built withcontinuous integration and continuous deployment.InstallationInstall with pip:pip install pyphmInstall from github repository: clone with gitclone https://github.com/tvhahn/PyPHM.git. Then runpython -m pip install -e .to install the package on your local machine.Run tests:python -m unittest discover -s tests
Package: py-phone-number-fmt
py-phone-number-fmtSanitize, validate and format phone numbers into E.164 valid phone numbers.Google's libphonenumber on steroids. Tries all sorts of crazy combinations in an attempt to create a valid phone number. Useful for those of us who have to deal with poorly sanitized data.See the list of test casesInstallation$ pip install py-phone-number-fmtUsagefrom phonenumberfmt import format_phone_number

# implied phone region is the country of which to
# use the dialing prefix in case the number appears
# to be local
result = format_phone_number('778\173 0.92', implied_phone_region='RO')
assert result == '+40778173092'The resulting phone number will be formatted according to the E.164 standard. Want to change the output format? Pass the third, optional parameterfmtwith a valid member ofphonenumbers.NumberFormat:from phonenumbers import NumberFormat
result = format_phone_number(
    '778\173 0.92',
    implied_phone_region='RO',
    fmt=NumberFormat.INTERNATIONAL, # default is NumberFormat.E164
)
Package: pyphonetics
Pyphonetics is a Python 3 library for phonetic algorithms. Right now, the following algorithms are implemented and supported:SoundexMetaphoneRefined SoundexFuzzy SoundexLeinMatching Rating ApproachMore will be added in the future.InstalationThe module is available in PyPI, just usepip install pyphonetics.Usage>>> from pyphonetics import Soundex
>>> soundex = Soundex()
>>> soundex.phonetics('Rupert')
'R163'
>>> soundex.phonetics('Robert')
'R163'
>>> soundex.sounds_like('Robert', 'Rupert')
TrueThe same API applies to every algorithm, e.g:>>> from pyphonetics import Metaphone
>>> metaphone = Metaphone()
>>> metaphone.phonetics('discrimination')
'TSKRMNXN'You can also use thedistance(word1, word2, metric=’levenshtein’)method to find the distance between 2 phonetic representations.>>> from pyphonetics import RefinedSoundex
>>> rs = RefinedSoundex()
>>> rs.distance('Rupert', 'Robert')
0
>>> rs.distance('assign', 'assist', metric='hamming')
2CreditsThe module was largely based on the implementation of phonetic algorithms found in the Talisman.js (https://github.com/Yomguithereal/talisman) Node NLP library.
Package: pyphony
pyphonyA library to work with phone lexica.Read and write lexica from/to a file.Convert between different formatsInstallationInstall the latest development version:pipinstallgit+https://github.com/ynop/pyphony.gitDevelopmentPrerequisitesA supported version of Python 3It's recommended to use a virtual environment when developing pyphony.
To create one, execute the following command in the project's root directory:python -m venv .To install pyphony and all it's dependencies, execute:pip install -e .Running the test suitepip install -e .[dev]
python setup.py testWith PyCharm you might have to change the default test runner. Otherwise, it might only suggest to use nose. To do so,
go to File > Settings > Tools > Python Integrated Tools (on the Mac it's PyCharm > Preferences > Settings > Tools >
Python Integrated Tools) and change the test runner to py.test.VersionsVersions is handled usingbump2version. To bump the version:bump2version [major,minor,patch,release,num]In order to directly go to a final relase version (skip .dev/.rc/...):bump2version [major,minor,patch] --new-version x.x.xReleaseCommands to create a new release on pypi.rm -rf build
rm -rf dist

python setup.py sdist
python setup.py bdist_wheel
twine upload dist/*
Package: pyphorus
PyphorusA small network utility library written in Python 3.Features:Port scannerUPnP client (SSDP wrapper)Build with :heart: byOleaintueriis sponsoring the development and maintenance of this project within their organisation.Getting startedInstallationpip install pyphorusOrpip install git+https://github.com/Oleaintueri/pyphorus.gitUsageimportpyphorusif__name__=="__main__":phorus=pyphorus.Pyphorus()devices=phorus.scan_ports("192.168.0.1",ports=[80,443,9000],only_open=True)fordeviceindevices:print(device.ip,device.port)# get all devices with ssdp:alldevices=phorus.scan_upnp("upnp:rootdevice")fordeviceindevices:print(device.ip,device.friendly_name,device.device_type)# if you want only the unique ips to remain and are not interested in the portsunique_devices=pyphorus.utils.strip_duplicate_ips(devices)TestingPyphorus usesnoseto run its tests and mock testing server.nosetests -v
Package: pyphot
This is a set of tools to compute synthetic photometry in a simple way, ideal to
integrate in larger projects.full documentation at:http://mfouesneau.github.io/pyphot/The inputs are photonic or energetic response functions for the desired
photometric bands and stellar spectra. The modules are flexible to handle units
in the wavelength definition through a simplified version ofpint(link) andastropy.unitsFilters are represented individually by aFilterobject. Collections of
filters are handled with aLibrary. We provide an internal library that
contains a signitificant amount of common filters.Each filter is minimally defined by awavelengthandthroughput. Many
properties such as central of pivot wavelength are computed internally. When
units are provided for the wavelength, zero points in multiple units are also
accessible (AB, Vega magnitude, Jy, erg/s/cm2/AA). The default detector type is
assumed to be photonic, but energetic detectors are also handled for the
computations.Notecphotis a spin-off project that provides a C++ version of pyphotWhat’s new?[November 22, 2021] new filters, SVO interface, automated tests and documentation.[November 6, 2019] astropy version available in beta (from pyphot import astropy as pyphot)[April 29, 2019] sandbox contains fully unit aware passbands and lick indices libraries[April 15, 2019] merged UncertainFilter to main, sandbox contains passbands accounting for spectrum units[March 4, 2019] added flux calculations in photon/s/cm2[March 4, 2019] added many properties per filter (lphot, lmin, lmax)[June 12, 2018] adding Sun reference spectra (see:class:Sun)[Apr. 26, 2018] includes Gaia nominal, DR2 and revised DR2 passbandsInstallationBefore installation, make sure you have HDF5 version 1.8.4 or above (this is required for pytables, see details at:https://github.com/PyTables/PyTables).For OSX:brew install hdf5For Debian-based distributions:sudo apt-get install libhdf5-serial-devInstallation from PyPIpip install pyphotManual installationdownload the repository and run the setuppython setup.py installContributorsAuthor:Morgan FouesneauDirect contributions to the code base:Tim Morton (@timothydmorton)Ariane Lancon (@lancon)
Package: pyphoton
🗺 Light Photon client written in PythonInstallpipinstallpyphotonUsageIf you need some code ready to use,spike.pyis a good starting pointExecute queriesPython Photon client allows you to make queries to Photon service
easily.frompyphotonimportPhotonclient=Photon()location=client.query('berlin',limit=1)print(location.city)print(location.latitude)print(location.longitude)Photonobject accepts two parameters:host: the url where Photon instance is running (defaulthttps://photon.komoot.de)language: the preferred language (default isen)You can pass thequerymethod the following parameters along the
query string:limit: limit number of resultslanguage: force language in the querylatitudeandlongitude: use them to search with priority to a
geo positionlocation_bias_scale: use to search with location biasosm_tags: astringorlistcontainingosm tags
filtersbbox: astringwith comma-separated values orlistcontainingbounding box
coordinatesLocationobject (or objects if you don’t set limit=1) is generated
from the json returned and contains all the information you need: name,
state, street, city, osm attributes, extent_from.latitude,
extent_from.longitude, extent_to.latitude, extent_to.longitude …Reverse searchPython Photon client allows you to make reverse search.frompyphotonimportPhotonclient=Photon()locations=client.reverse(latitude=52,longitude=10)forlocationinlocations:print('🌉 Location #{0}\n{1}\n'.format(location.osm_id,location))You can pass to thereversemethod the following parameters:latitudeandlongitude: use them to search using a geo
positionlimit: limit number of resultsDeal with errorsIf there’s an error in your query, aPhotonExceptionwill be raisedfrompyphotonimportPhotonfrompyphoton.errorsimportPhotonExceptionclient=Photon()try:location=client.query('',limit=1)exceptPhotonExceptionasex:print(ex)Run testspipinstall-rrequirements-dev.txtmaketestWIP FeaturesMethod to fetchlatest
data
Package: pyphotonfile
pyPhotonfile is a library used for manipulating Photon-files created for the Anycubic Photon 3D-Printer. Currently it supports removing and adding new layers as well as changing global parameters like the exposure time, etc.
It is based on the work done byPhotonFileEditor. While PhotonFileEditor works, I was in need of a clean library which is why I refactored most of the code.
pyPhotonfile is the backbone ofSL1toPhoton.Friendly ReminderUse at your own risk. Please verify that what you are doing will not break your printer.Example UsagefrompyphotonfileimportPhotonphoton=Photon('in_file.Photon')forlayerinphoton.layers:print(layer)photon.export_images('tempdir')photon.delete_layers()forfilepathinos.listdir('tempdir'):photon.append_layer(filepath)photon.exposure_time=10photon.bottom_layers=3photon.write('out_file.Photon')