Saravanan666/pypi-org huggingface dataset:
Package: signup-genius
signup-geniusA library for parsing signup-genius responses from the signup html, as a free alternative to a paid Signup Genius subscription.Installpipinstallsignup-geniusUsagefromsignup_geniusimportget_signupssignups=get_signups('https://www.signup-genius.com/go/xyz...')The signup objects returned will vary depending on the type of signup genius encountered.For example for a simple yes/no response with adult / child counts the signup will look like:classSignup:name:strresponse:strcomments:str=''adult_count:int=0child_count:int=0Whereas for a signup-genius with Date / Location / Time and multiple slots with capacities, it will look like:classSignup:name:strcomments:str=''count:int=0slot:Slot# where the embedded slot object is:classSlot:date:strlocation:strtime:strname:strcomments:str=""capacity:int=1filled:int=0Name MappingOften times it is useful to coerce the signup names returned to a known set of names, e.g. a club or organization whose menbers are known but where there may be variations in how those names are entered in the signup genius ... Jeff vs. Jeffrey, amy vs Amy, etc.2 Options are supported:An explicit mapping from what is entered to the final representationmapping={'Jeff Adams':'Jeffrey Adams'}signups=get_signups(url,name_mapping=mapping)Where a signup is updated, an_original_nameattribute will be added to the object allowing you to detect after the fact which signups have been updatedFuzzy matching against a population of namesYou can supply a population of known names, and any names not matching this population will be mapped to the closest match (using Levenshtein distance).known_names=['Jeffrey Adams','John Doe',AmyMalone']signups=get_signups(url,match_names=known_names)Again the signups that are updated will be annotated with an_original_nameattribute.Signup Genius Variants supportedSo far the following variants are supported:Yes / No / Maybe responses with guest countYes / No / Maybe responses with adult, child countsDate / Time / Slot signupsDate / Location / Time / Slot signupsThe call to get_signups will first inspect the signup genius to interpret the variant, and use the appropriate parser.  If the variant is not known then aUnsupportedVariantexception will be raised
Package: signupto
Minimalist client library for the sign-up.to HTTP API -http://sign-up.toFree software: BSD licenseHome page:https://bitbucket.org/spookylukey/signuptoBugs:https://bitbucket.org/spookylukey/signupto/issues?status=new&status=openDocs:http://signupto.readthedocs.org/en/latest/StatusThis is beta software. It is being used in production, and covers the complete
sign-up.to HTTP API. There may still be improvements and fixes to be made.If you use this library, let me know (atL.Plant.98@cantab.net), and I can
consult you about improvements. Otherwise I may change things without notice.There are also no automated tests yet. (Patches welcome!)History0.1 (2013-11-21)Cleanups0.0.6 (2013-11-14)Added convenience methodsget_list,get_allanddelete_any.0.0.5 (2013-11-13)Python 3 fixes0.0.4 (2013-11-13)Rewrote to remove drest dependency. It’s simpler and shorter now.0.0.3 (2013-11-12)Fixed put methods0.0.2 (2013-11-12)Fixed delete methods0.0.1 (2013-11-12)First release on PyPI.
Package: signway-sdk
Signway Python SDKGenerate signed urls forSignway, so that they can be
used in client-side code.Installpipinstallsignway-sdkUsagefromsignway_sdkimportsign_url

sign_url(id="my-id",secret="my-secret",host="https://api.signway.io",proxy_url="https://api.openai.com/v1/chat/completions",expiry=10,method="POST")
Package: signxml_sunat
SignXML Sunatis an implementation of the W3CXML Signaturestandard in
Python.SignXML Sunatimplements all of the required components of the standard, and most recommended ones. Its features are:Well-supported, portable, reliable dependencies:lxml,xmlsecSimple interface with useful defaultsCompactness, readability, and extensibilityInstallationpip install signxml_sunatNote: SignXML SUNAT depends onlxmlandxmlsecOSPythonCommandUbuntu 16.04,
18.04Python 3pip install lxml xmlsecNote: In windows you need install a binary for lxml and xmlsechttps://www.lfd.uci.edu/~gohlke/pythonlibsSynopsisSignXML SUNAT uses the ElementTree API (also supported by lxml) to work with XML data.fromsignxml_sunatimportXMLSigner,XMLVerifiersigned_root=XMLSigner().sign('doc.xml',key_data='rsakey.pem',cert_data='rsacert.pem')# if you need password# signed_root = XMLSigner().sign('doc.xml', key_data='rsakey.pem', cert_data='rsacert.pem', password='p4ssw0rd')verified=XMLVerifier().verify('signed_doc.xml',key_data='rsakey.pem')# if you need password# verified_data = XMLVerifier().verify('signed_doc.xml', key_data='rsakey.pem', password='p4ssw0rd')AuthorsDanilo Britto NuñezBugsPlease report bugs, issues, feature requests, etc. onGitHub.
Package: sign-xpi-lib
sign-xpi-libA library to handle the manipulations of signing XPIs at Mozilla.Free software: MIT licenseDocumentation:https://sign-xpi-lib.readthedocs.io.OverviewInformation about how XPI signing works in Firefox can be found atthe Mozilla wiki.A tool that generates PKCS7 signatures in the correct format isautograph, which
see for more information.This library is used bythe sign-xpi lambda, but can be used by
other clients too.Usage:from sign_xpi_lib import XPIFile

x = XPIFile('hypothetical-addon-unsigned.xpi')

# this is the mozilla.sf file computed by hashing mozilla.rsa
signed_manifest = x.signed_manifest
print(signed_manifest)

# This probably talks to Autograph or an HSM or whatever
signature = 'generate-a-signature somehow'

x.make_signed('hypothetical-addon-signed.xpi', 'mozilla.rsa',
              signed_manifest, signature)See thetestsfor more details.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2017-07-07)First release on PyPI.
Package: sigparse
sigparseBackports python3.10 typing features into python 3.7, 3.8, and 3.9.Exampleimportsigparsedeffunc(param_a:list[str],param_b:str|int,param_c:tuple[int|None]):...# This returns the same result in python 3.7, 3.8, 3.9, and 3.10!sigparse.sigparse(func)Sigparse also supports classes.importsigparseclassMyClass:a:list[str]b:str|intc:tuple[int|None]sigparse.classparse(MyClass)PEP 604By default PEP 604 (| for unions) is only enabled forsigparse.sigparseandsigparse.classparse.
The code for PEP 604 was written by@Endercheif.To enable globally:importsigparsesigparse.global_PEP604()NotesInspectThis module uses inspect behind the scenes. For that reason:All annotations areinspect._emptywhen there is no annotation.sigparse.Parameter.defaultisinspect._emptywhen there is no default value.sigparse.Parameter.kindisinspect._ParameterKind.sigparse.ClassVar.defaultisinspect._emptywhen there is no default value to maintain consistency.Annotatedtyping.Annotatedwill always be evaluated withinclude_extras=Truein python3.9.
Package: sigpropy
sigpropy - A Python package for signal processingJoseph Vantassel, The University of Texas at AustinTable of ContentsAboutsigpropyTimeSeriesFourierTransformAboutsigpropysigpropyis a Python package for digital signal processing. It includes two
main class definitions,TimeSeriesandFourierTransform. These classes
include methods to perform common signal processing techniques (e.g., trimming
and resampling) and properties to make using them readable and intuitive.This package and the classes therein are being used in several other
Python projects, some of which have been released publicly and others are
still in the development stage, so if you do not see a feature you would like
it may very well be under development and released in the near future. To be
notified of future releases, you can eitherwatchthe repository onGitHuborSubscribe to releaseson thePython Package Index (PyPI).TimeSeriesA simple example:importsigpropyimportmatplotlib.pyplotaspltimportnumpyasnpdt=0.002time=np.arange(0,1,dt)s1=1*np.sin(2*np.pi*10*time)s2=2*np.sin(2*np.pi*20*time)s3=5*np.sin(2*np.pi*30*time)amplitude=s1+s2+s3tseries=sigpropy.TimeSeries(amplitude,dt)fseries=sigpropy.FourierTransform.from_timeseries(tseries)plt.plot(tseries.time,tseries.amplitude)plt.xlabel("Time (s)")plt.ylabel("Amplitude")plt.show()FourierTransformA simple example:importsigpropyimportmatplotlib.pyplotaspltimportnumpyasnpdt=0.002time=np.arange(0,1,dt)s1=1*np.sin(2*np.pi*10*time)s2=2*np.sin(2*np.pi*20*time)s3=5*np.sin(2*np.pi*30*time)amplitude=s1+s2+s3tseries=sigpropy.TimeSeries(amplitude,dt)fseries=sigpropy.FourierTransform.from_timeseries(tseries)plt.plot(fseries.frequency,fseries.mag)plt.xscale("log")plt.xlabel("Frequency (Hz)")plt.ylabel("|FFT Amplitude|")plt.show()Special Thanks ToAlbert Kottkefor his suggestions to speed up the Konno and Ohmachi
smoothing. For a standalone implementation of Konno and Ohmachi smoothing see
his projectpykooh.
Package: sigproSS
SigProfilerSingleSampleSigProfilerSingleSample allows attributing a known set of mutational signatures to an individual sample. The tool identifies the activity of each signature in the sample and assigns the probability for each signature to cause a specific mutation type in the sample. The tool makes use of SigProfilerMatrixGenerator, SigProfilerExtractor and SigProfilerPlotting.INSTALLATIONIn the commandline, please type the following line:$pip install sigproSSInstall your desired reference genome from the command line/terminal as follows (available reference genomes are: GRCh37, GRCh38, mm9, and mm10):$ python
>> from SigProfilerMatrixGenerator import install as genInstall
>> genInstall.install('GRCh37')This will install the human 37 assembly as a reference genome. You may install as many genomes as you wish.open a python interpreter and import the SigProfilerExtractor module. Please see the examples of the functions.FUNCTIONSimportdataImports the path of example data.

importdata()

Example: 
-------
>>> from sigproSS import spss 
>>> data = spss.importdata()
This "data" variable can be used as a parameter of the first argument of the sigproSS function.

To get help on the parameters and outputs of the "importdata" function, please write down the following line:

>>> help(spss.importdata)single_sampleDecompose the query samples into the global signatures.

single_sample(vcf, outputdir, exome=False)

Example: 
-------
>>> from sigproSS import spss 
>>> data = spss.importdata()
>>> spss.single_sample(data, "results", ref="GRCh37", exome=False)

To get help on the parameters and outputs of the "importdata" function, please write down the following line:

>>> help(spss.single_sample)COPYRIGHTThis software and its documentation are copyright 2018 as a part of the sigProfiler project. The SigProfilerExtractor framework is free software and is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.CONTACT INFORMATIONPlease address any queries or bug reports to S M Ashiqul Islam (Mishu) at m0islam.ucsd.edu
Package: sigpy
Source Code|Documentation|MRI Recon Tutorial|MRI Pulse Design TutorialSigPy is a package for signal processing, with emphasis on iterative methods. It is built to operate directly on NumPy arrays on CPU and CuPy arrays on GPU. SigPy also provides several domain-specific submodules:sigpy.plotfor multi-dimensional array plotting,sigpy.mrifor MRI reconstruction, andsigpy.mri.rffor MRI pulse design.InstallationSigPy requires Python version >= 3.5. The core module depends onnumba,numpy,PyWavelets,scipy, andtqdm.Additional features can be unlocked by installing the appropriate packages. To enable the plotting functions, you will need to installmatplotlib. To enable CUDA support, you will need to installcupy. And to enable MPI support, you will need to installmpi4py.ViacondaWe recommend installing SigPy throughconda:conda install -c frankong sigpy
# (optional for plot support) conda install matplotlib
# (optional for CUDA support) conda install cupy
# (optional for MPI support) conda install mpi4pyViapipSigPy can also be installed throughpip:pip install sigpy
# (optional for plot support) pip install matplotlib
# (optional for CUDA support) pip install cupy
# (optional for MPI support) pip install mpi4pyInstallation for DevelopersIf you want to contribute to the SigPy source code, we recommend you install it withpipin editable mode:cd /path/to/sigpy
pip install -e .To run tests and contribute, we recommend installing the following packages:pip install coverage ruff sphinx sphinx_rtd_theme black isortand run the scriptrun_tests.sh.FeaturesCPU/GPU Signal Processing FunctionsSigPy provides signal processing functions with a unified CPU/GPU interface. For example, the same code can perform a CPU or GPU convolution on the input array device:# CPU convolvex=numpy.array([1,2,3,4,5])y=numpy.array([1,1,1])z=sigpy.convolve(x,y)# GPU convolvex=cupy.array([1,2,3,4,5])y=cupy.array([1,1,1])z=sigpy.convolve(x,y)Iterative AlgorithmsSigPy also provides convenient abstractions and classes for iterative algorithms. A compressed sensing experiment can be implemented in four lines using SigPy:# Given some observation vector y, and measurement matrix matA=sigpy.linop.MatMul([n,1],mat)# define forward linear operatorproxg=sigpy.prox.L1Reg([n,1],lamda=0.001)# define proximal operatorx_hat=sigpy.app.LinearLeastSquares(A,y,proxg=proxg).run()# run iterative algorithmPyTorch InteroperabilityWant to do machine learning without giving up signal processing? SigPy has convenient functions to convert arrays and linear operators into PyTorch Tensors and Functions. For example, given a cupy arrayx, and aLinopA, we can convert them to Pytorch:x_torch=sigpy.to_pytorch(x)A_torch=sigpy.to_pytorch_function(A)
Package: sigpy-lite
NOTE:This is a fork of the repository below. Changes have been made to lighten it’s dependency on Numba and PyWavelets. If you are using a function that used Numba either install with the[full]extra or expectmuchslower results.SigPySource Code|Documentation|MRI Recon Tutorial|MRI Pulse Design TutorialSigPy is a package for signal processing, with emphasis on iterative methods. It is built to operate directly on NumPy arrays on CPU and CuPy arrays on GPU. SigPy also provides several domain-specific submodules:sigpy.plotfor multi-dimensional array plotting,sigpy.mrifor MRI reconstruction, andsigpy.mri.rffor MRI pulse design.InstallationSigPy requires Python version >= 3.5. The core module depends onnumba,numpy,PyWavelets,scipy, andtqdm.Additional features can be unlocked by installing the appropriate packages. To enable the plotting functions, you will need to installmatplotlib. To enable CUDA support, you will need to installcupy. And to enable MPI support, you will need to installmpi4py.ViacondaWe recommend installing SigPy throughconda:conda install -c frankong sigpy
# (optional for plot support) conda install matplotlib
# (optional for CUDA support) conda install cupy
# (optional for MPI support) conda install mpi4pyViapipSigPy can also be installed throughpip:pip install sigpy
# (optional for plot support) pip install matplotlib
# (optional for CUDA support) pip install cupy
# (optional for MPI support) pip install mpi4pyInstallation for DevelopersIf you want to contribute to the SigPy source code, we recommend you install it withpipin editable mode:cd /path/to/sigpy
pip install -e .To run tests and contribute, we recommend installing the following packages:pip install coverage ruff sphinx sphinx_rtd_theme black isortand run the scriptrun_tests.sh.FeaturesCPU/GPU Signal Processing FunctionsSigPy provides signal processing functions with a unified CPU/GPU interface. For example, the same code can perform a CPU or GPU convolution on the input array device:# CPU convolvex=numpy.array([1,2,3,4,5])y=numpy.array([1,1,1])z=sigpy.convolve(x,y)# GPU convolvex=cupy.array([1,2,3,4,5])y=cupy.array([1,1,1])z=sigpy.convolve(x,y)Iterative AlgorithmsSigPy also provides convenient abstractions and classes for iterative algorithms. A compressed sensing experiment can be implemented in four lines using SigPy:# Given some observation vector y, and measurement matrix matA=sigpy.linop.MatMul([n,1],mat)# define forward linear operatorproxg=sigpy.prox.L1Reg([n,1],lamda=0.001)# define proximal operatorx_hat=sigpy.app.LinearLeastSquares(A,y,proxg=proxg).run()# run iterative algorithmPyTorch InteroperabilityWant to do machine learning without giving up signal processing? SigPy has convenient functions to convert arrays and linear operators into PyTorch Tensors and Functions. For example, given a cupy arrayx, and aLinopA, we can convert them to Pytorch:x_torch=sigpy.to_pytorch(x)A_torch=sigpy.to_pytorch_function(A)
Package: sigpytch
SIGPytchQuantitative models and metrics used in the SIG at UCSD quarterly stock pitches.Table of ContentsInstallationRequirementsMetricsModelsContributingInstallationpip install sigpytchRequirementsThis library requires the following dependencies:pandas>=1.4.0scikit-learn>=1.1.13tensorflow>=2.11.0Metricssigpytch.metricsrolling_sharpeModelssigpytch.forecastersLSTMForecasterContributingReach out to Pranay to be added as a contributor to this repository.
Package: sigr
sigrDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-nsigr--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallsigrnbdev# activate conda environment$condaactivatesigr# make sure the sigr package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the sigr package$nbdev_preparePublishing# publish to pypi$nbdev_pypi# publish to conda$nbdev_conda--build_args'-c conda-forge'$nbdev_conda--mambabuild--build_args'-c conda-forge -c dsm-72'UsageInstallationInstall latest from the GitHubrepository:$pipinstallgit+https://github.com/dsm-72/sigr.gitor fromconda$condainstall-cdsm-72sigror frompypi$pipinstallsigrDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.
Package: sig-saturate
sig_saturateSecond-Order Lag System with Saturation
Highlights:Set system parameteers for second order lag systemSet upper and lower saturation limitsslect from different types of input signalSet / change the properties of input signalVisualise the input and output signalChechout the Demo hosted atLinkInstallationPypirun following command in terminalpipinstallsig-saturateFrom sourceRun following command in terminalgit clone https://github.com/bokey007/sig_saturatecd sig_saturatepython setup.py sdist bdist_wheelpip install ./dist/sig_saturate-0.1.0.tar.gzUsagesig_saturate.runAbove command will lauch the app on default port 8501.Open the browser and go tohttp://localhost:8501play with the parameters interatively and visualise input and output signals from different sets of parameters.sig_saturate.run--port8080Above command can be used to specify the port on which you want to run the app.DemoSolution is implemnted in following three stepsGenerate input siganls as per user inputsSimulates the behavior of a second-order lag system with saturationVisualization the input and outputDevelopment tools:setuptools (https://pypi.org/project/setuptools/): Used to create a python packagepipreqs (https://pypi.org/project/pipreqs/): Used to create requirements.txt filetwine (https://pypi.org/project/twine/): Used to upload the package to pypi.orgwheel (https://pypi.org/project/wheel/): Used to create a wheel file
Package: sigtools
Thesigtoolspython library provides:sigtools.signatureto resolve function signatures,
taking into account parameters added or removed by decorators.Decorators to specify keyword-only parameters, annotations and
positional-only parameters, even on python2:sigtools.modifiersDecorators to specify how*args, **kwargsare handled, in a way
that can be introspected:sigtools.specifiersFunction combination routines that preserve signatures:sigtools.wrappersFunctions to manipulate signature objects likewise:sigtools.signaturesThe documentation can be found at Read The Docs:https://sigtools.readthedocs.orgInstallingWithin avirtualenv:$ pip install sigtoolsOr for a user-wide install:$ pip install --user sigtools
Package: sigurd
Nibelungenlied and Völsunga sagaComparison betweenNibelungenliedandVölsunga sagaThe name of the package issigurd.Slides and code are available atwww.clementbesnier.fr/projets/cltk_2019_graz.Download and Installation$pipinstallsigurdOrigins of the dataVölsunga sagaFor Old Norse texts:https://github.com/clemsciences/old_norse_corpusand ultimatelyhttps://heimskringla.no/wiki/Main_Page.Referenzkorpus MittelhochdeutschSource and licenseMain page of the projectKlein, Thomas; Wegera, Klaus-Peter; Dipper, Stefanie; Wich-Reif, Claudia (2016). Referenzkorpus Mittelhochdeutsch (1050–1350), Version 1.0,https://www.linguistics.ruhr-uni-bochum.de/rem/. ISLRN 332-536-136-099-5.License:Das Referenzkorpus Mittelhochdeutsch ist lizenziert unter einerCreative Commons Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International Lizenz.No change is made on the corpus. This code is intended to parse the corpus.Corpus retrievalGo tohttps://www.linguistics.rub.de/rem/access/index.html.Click on "CORA-XML AKS .TAR.XZ" or "CORA-XML ALS .ZIP"Click on "Herunterladen".Uncompress the dowloaded file.You have a folder, namedrem-corraled-20161222(2019-09-18) with a list of XML files which are annotated texts.
Package: sihm
Standalone Interactive HTML Movie (SIHM)A highly-portable, interactive way to visualize your simulations.Summary|Examples|Installation guideSummaryThis package providessihm, a command-line tool that takes as input a YAML file that defines geometry and properties of that geometry—position, velocity, color, etc.—vs. time and turns it into a standalone, interactive HTML movie.The output is a movie in the sense that the trajectories vs. time are fixed. You'll be able to pause, play, rewind, loop, etc. as a movie would.The output is interactive because you can move the camera around while the video is playing (or paused). You can also "attach" the camera to an object and follow that object around the sim.The output is a standalone HTML file, i.e., it is the only file you will need to run the interactive movie.HTML was selected because it is extermely portable. The output should work anywhere: Linux, Mac, Windows, your cell phone, etc.ExamplesExamples ofsihm's output can be foundhere.InstallationLinuxInstall the python package:pip install sihmInstall the appropriate package—rpmordeb, depending on you OS— from the latest release.MacInstall the python package:pip install sihmDownload and install the brew package from the latest release, e.g.,brew install sihm*.rb.WindowsInstall the python package:pip install sihmInstall the system dependenciesyarnnodejs >= 16cmake
Package: siilo
Siilo is a file storage abstraction layer for Python. It is inspired byDjango’s File storage API, but is framework agnostic.Siilo supports for the following file storages:Local FilesystemApache LibcloudAmazon S3Siilo has the following goals:to be compatible with Python’s file APIto support both Python 2 and 3to have full unit test coverage.You can install the library with pip:pip install siiloResourcesDocumentationBug TrackerCodeDevelopment VersionChangelogHere you can see the full list of changes between each Siilo release.0.1.0 (April 25th, 2014)Initial public release.