Saravanan666/pypi-org huggingface dataset:
Package: pshub
# pshub`pshub` is a lightweight Python framework that partially implements Publish/Subscribe messaging paradigm.It utilizes `asyncio` library to support concurrent requests handling. The Python version that we recommend is 3.5+ .## NameThe name `pshub` indicates the three main components of this library — Pub, Sub and Hub.## UsageYou can refer to the three example files under directory `examples/`.## Cheatsheet### Rule#### Example`{"level": {">=": 1, "<": 10}, "message_body": {"contains": "ERROR"}}`#### Valid Operators- `contains`- `<`- `<=`- `>`- `>=`- `=`- `!=`- `excludes`### Message#### Example`{"level": 2, "message_body": "ERROR occurs"}`## Caveats- All messages/rules should be Python `dict` whose keys and values are primitive Python type (int, string, etc.)- The second parameter of the constructor of `PubProtocol`, `msg_gen`, should has following method:```pythonclass MessageGenerator(object):def next(self, loop):"""Return next messageStop the loop if there's no message to publish."""pass```## LicensePlumbum - A Pub/Sub framework implemented in PythonCopyright (C) 2016  caizixian, lwherThis program is free software: you can redistribute it and/or modifyit under the terms of the GNU Affero General Public License as publishedby the Free Software Foundation, either version 3 of the License, or(at your option) any later version.This program is distributed in the hope that it will be useful,but WITHOUT ANY WARRANTY; without even the implied warranty ofMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See theGNU Affero General Public License for more details.You should have received a copy of the GNU Affero General Public Licensealong with this program.  If not, see <http://www.gnu.org/licenses/>.
Package: psiaudio
psiaudioTools for auditory signal generation and processing. Provides:Finite and infinite signal generation supportCalibrationData processing pipelines using coroutinesBasic utility functions common to auditory processingContributors and AcknowledgementsBrad Buran (New York University, Oregon Health & Science University)Decibel Therapeutics, Inc.Work on psiaudio was supported by grants R01-DC009237 and R21-DC016969 from the National Institute on Deafness and Other Communication Disorders and an Emerging Research Grant from the Hearing Health Foundation.
Package: psicalc
PSICalc Algorithm PackageThis is a package for clustering Multiple Sequence Alignments (MSAs) utilizing normalized mutual information to examine protein subdomains. A complete data visualization tool for psicalc is available on the releases page.As an example:import psicalc as pc

file = "<your_fasta_file>" # e.g "PF02517_seed.txt"

data = pc.read_txt_file_format(file) # read Fasta file

data = pc.durston_schema(data, 1) # Label column index starting at 1

result = pc.find_clusters(1, data) # will sample every column against msa

# Optionally write dictionary to csv
pc.write_output_data(1, result)The program will run and return a csv file with the strongest clusters found in the MSA provided.
Package: psi-cli
psiflutter-rs devtoolUsagepsi create flutter_app
Create a flutter-rs apppsi create .
Add flutter-rs to existing flutter projectpsi run --vscode
Run a flutter-rs app in dev modepsi build {nsis,mac,dmg,snap} --release
Bundle a flutter-rs app for distributionpsi precache
Precache flutter engine libraryInstallThis python package is installed by:pip install psi-cliYou'll also need to install components required byflutter-rsin order to use it.
Package: psi-collect
Post-Storm Imagery CollectionCollection, aggregation, and cataloging of NOAA post-storm emergency response imagery.This package helps users (researchers, managers, etc.) download, analyze, and store aerial imagery taken after hurricane events that have impacted the USA.Team MembersMatthew MoretzDaniel FosterJohn WeberRinty ChowdhuryShah Nafis RafiqueEvan GoldsteinSomya MohantyUsageQuick Start:Install Python 3.6, 3.7, or 3.8(Download Here)Runpip3 install psi-collectin your favorite terminalRunpstorm collect -hfor help on collecting images orpstorm catalog -hfor help on cataloging local archivesCheck out the documentation forCollectingandCatalogingimages to see some examplesContributing / DevelopingCode of ConductWe hope to foster an inclusive and respectful environment surrounding the contribution and discussion of our project.
Make sure you understand ourCode of Conduct.Code ConventionsBefore committing to the repositorypleaseread the projectCode Conventions.Project Pre-RequisitesPython 3.6, 3.7, or 3.8(Download Here)Pipenv(Runpip install pipenv)You will need to add the module path<parent dir>/psi-collect/psicollect/to yourPYTHONPATH(SeeConfiguring Module)If you get an error message that looks something likeModule not found: "psicollect",
then thePYTHONPATHis not configured correctly!You will need this in order to run the project.Installing Dependencies for DevelopmentChange current directory (cd) topsi-collect/(the project root)Runpipenv install --devto install dependenciesRunpre-commit installto install style checking when committingTesting the Collect ScriptChange current directory to the collector module (cd collector/)Either usepipenv run collect.py <args>orpipenv shellthencollect.py <args>In addition tests can be run usingpytestfrom the project root directoryThe arguments forcollect.pyare listedhereData Source 💾NOAA landing page for the post-storm Emergency Response Imagery,here
Package: psidialogs
psidialogs (Python Simple Dialogs) is a common API
for different standard dialogs like message, ask_string,..Links:home:https://github.com/ponty/psidialogsdocumentation:http://ponty.github.com/psidialogsFeatures:unicode supportcross-platform, development on linuxback-ends:PyGTKZenityeasyguigMessagePyQtTkInterwxPythonPythonDialogconsoleEasyDialogsKnown problems:Python 3 is not supportedtested only on Linux and WindowsSome dialogs are too simple, because a common basic
implementation is used where implementation is missing.Basic usage>>> from psidialogs import message
>>> message('Hello!')InstallationGeneralinstallpipinstall the program:# as root
pip install psidialogsUbuntusudo apt-get install python-pip
sudo pip install psidialogs
# optional back-ends
sudo apt-get install python-gtk2
sudo apt-get install python-tk
sudo apt-get install python-qt4
sudo apt-get install python-dialog
sudo apt-get install zenity
sudo apt-get install gxmessage
sudo apt-get install python-easygui
sudo pip install --no-deps easydialogs-gtkUninstall# as root
pip uninstall psidialogssimilar projectsanygui: multiple back-endseasygui: tk back-endPyZenity: Zenity back-endvsgui: Zenity back-enddlg: dialog/Xdialog/gdialog  back-endpython-dialog: dialog/Xdialog/gdialog  back-endeasydialogs-gtk: EasyDialogs API, PyGTK back-endEasyDialogs: EasyDialogs API, Mac back-endEasyDialogs for Windows: EasyDialogs API, Windows back-end
Package: psi-dlpa
Client and server applications providing Distributed Laplace
Perturbation Algorithm (DLPA).The DLPA has been introduced byVibhor
RastogiandSuman Nathin “Differentially Private Aggregation of Distributed Time-Series with
Transformation and
Encryption,” and
published inSIGMOD 2010.ClientThis package has client classdlpa.DLPAClient. To create an
instance, the constructor takes two arguments:host: Address of a DLPA server,port: Port number of the DLPA server.Although the client class implements several protocols defined in DLPA,get_keyandencrypt_noisy_sumare the only method users might
have interest.Theget_keyrequests a client key to the server. It takes one
argument, client ID, and returns a client key object.The other methodencrypt_noisy_sumruns the Entryp-Noisy-Sum
protocol, which is the key protocol of DLPA. It takes the following four
arguments:ck: Client key.client_id: Client ID.value: Scalar or vector to be sent to the server by Encrypt-Sum
protocol.epsilon: Parameter to generate Laplace noises.and returns a time slot when the request is attached.Serverdlpa-servercommand runs a server application of DLPA. The following
is the usage of this command:usage: dlpa-server [-h] --port PORT --clients NCLIENT
                   [--max-workers MAX_WORKERS] [--key-length M_LENGTH]
                   [--time-span SPAN]

optional arguments:
  -h, --help            show this help message and exit
  --port PORT           Listening port number.
  --clients NCLIENT     The number of clients.
  --max-workers MAX_WORKERS
                        The maximum number of workers (default: 10).
  --key-length M_LENGTH
                        Bit length of the secret key (default: 2048).
  --time-span SPAN      Second of one time slot.Note that the port number and the number of clients are necessary
arguments.You can also starts a server from another python script by usingdlpa.serverfunction. The function takes the following keyword
arguments:port: Port number the created server listen.max_workers: The maximum number of workers.nclient: The number of clients connecting this server.m_length: Bit length of the secret key.and returns a server object, which has a methodstop(t)to stop the
server within the given timet. Callers are responsible for calling
the method to close the server.Installation$ pip install --upgrade psi-dlpaAdditionaly, if you installgmpy2, computation time will be
reduced. To install gmpy2 in mac, you also need to install mpc, mpfr,
and libmp. Those three packages are available inHomebrew.LicenseThis software is released under The GNU General Public License Version
3, seeCOPYINGfor more detail.The functions, powmod, invert, and getprimeover, defined insrc/dlpa/util.pyare made by Data61 | CSIRO and released under the
GPLv3. The original source code ishere.
Package: psid_py
psid_pyA python module to build PSID data sets in Python.This module is based on the psidR package by Florian Oswald, avalable at:https://github.com/floswald/psidR/Aboutpsid_pyis a package to make building panel data sets from the PSID database fast and easy.  The package includes functionality to download the raw ASCII files for you, to parse the ASCII into a dataframe, and to build a panel based on user specifications.  Specifications include subsampling from SRC, SEO, immigrant, or latino subsamples, as well as by head of household only.  Finally, the user can specify a balanced panel or a panel consisting of individuals present for a specific number of years.UsabilityThe package comes with atest.pyfile that contains typical function calls and the proper way to define inputs.More testing is forthcoming in future distributions.The project can be downloaded fromhttps://pypi.python.org/pypi/psid_py.To install simply runpip install psid_pyThis project is released under an MIT license.
Package: psifospoll
PsifosPoll: Library for Election Result AlgorithmsPsifosPoll is a Python library developed by the electronic voting system Psifos team. For more information about our work, visit the Psifos Participa UChile instance page:Participa UChile.DescriptionPsifosPoll is a Python library designed to contain different algorithms for calculating the result of an election.Currently, the library includes the algorithm for preferential ranking voting,Single Transferable Vote (STV), in itsWeighted Inclusive Gregory Methodversion with adroop quota.ResourcesSTV Versions: For more information about different versions of STV, it is recommended to consult the paperDETERMINING THE RESULT: Transferring Surplus Votes in the Western Australian Legislative CouncilPsifosPoll Implementation: If you want to learn more about the version of STV implemented by this library, we suggest reviewing the paperShuffle-Sum: Coercion-Resistant Verifiable Tallying for STV Votingin section I.c.InstallationYou can install PsifosPoll using the pip package manager. Run the following command in your terminal:$pipinstallpsifospollUsage ExampleHere is a basic example of how to use PsifosPoll with STV:frompsifospollimportSTVElectiondefstvTally(seats,candidates_list,ballot_list):election=STVElection()election.runElection(seats,candidates_list,ballot_list)print(election.getRoundResumes())print(election.getTalliesResumes())print(election.getWinnersList())print(election.getQuota())# Voting example# Three seats, five candidates and six ballotsl1=[[1,2,3,4,5],[5,4,3,2,1],[2,3,4,5,1],[3,4,5,1,2],[4,5,1,2,3],[1,2,3,4,5],]cand=[1,2,3,4,5]stvTally(3,cand,l1)# In this example, STV runs for 3 rounds, and candidates 1, 3 and 4 are elected.More ExamplesYou can find more examples of possible inputs for STV in the examples.py file of the library.ContributionsWe would love to receive contributions! If you want to make a Pull Request (PR), make sure the code used is formatted with the black library. You can do this by running the following command in the PsifosPoll directory:black .ContactIf you have any questions, feel free to contact us atparticipa@uchile.clThank you for using PsifosPoll!
Package: psifr
PsifrAdvanced analysis and visualization of free recall data in Python.Features:A large library of advanced analyses, tested against published benchmarksFlexible analysis customization and plottingTools for exploratory analysis of large datasetsExtensive automated testing to ensure analysis correctnessBased around a simple and flexible table-based data formatComprehensivedocumentationanduser guideThe name Psifr is pronounced "cipher". It's taken from Psi, in reference to the field of psychology, and FR for free recall.Raster plot showing each recall in a free recall experiment. Color indicates serial position; yellow items were presented late in the list, while purple items were presented at the beginning. Magenta squares indicate intrusions of items that were not presented during the study list.InstallationYou can install the latest stable version of Psifr using pip:pipinstallpsifrYou can also install the development version directly from the code
repository on GitHub:pipinstallgit+https://github.com/mortonne/psifrQuickstartTo plot a serial position curve for a sample dataset:frompsifrimportfrdf=fr.sample_data('Morton2013')data=fr.merge_free_recall(df)recall=fr.spc(data)g=fr.plot_spc(recall)See theuser guidefor detailed documentation on importing and analyzing free recall datasets.Also see the Jupyter notebooks for more analysis examples:Recall performanceTemporal clusteringImporting dataGenerally the best way to get your data into shape for analysis in Psifr is to create a CSV (or TSV) file with one row for each event in the experiment, including study events (i.e., item presentations) and all recall attempts (including repeats and intrusions).
Seeimporting datafor details.A number of archival free recall datasets are available in the Matlab-based EMBAM format.
Data archives for a number of studies are available from theUPennandVanderbiltmemory labs.
If you have data inEMBAMformat, usematlab/frdata2table.mto convert your data struct to a table with standard format.
Then use the Matlab functionwritetableto write a CSV file which can then be read into Python for analysis.CitationIf you use Psifr, please cite the paper:Morton, N. W., (2020).
Psifr: Analysis and visualization of free recall data.
Journal of Open Source Software, 5(54), 2669,https://doi.org/10.21105/joss.02669Publications using PsifrHong, B., Barense, M. D., Pace-Tonna, C. A. & Mack, M. L. (2022).
Emphasizing associations from encoding affects free recall at retrieval.
Proceedings of the Annual Meeting of the Cognitive Science Society 453–460.https://escholarship.org/uc/item/2gw1s36qRelated projectsEMBAMAnalyses supported by Psifr are based on analyses implemented in the Matlab toolboxEMBAM.pybehpybehis a direct Python port of EMBAM that supports a wide range of analyses.QuailQuailruns automatic scoring of free recall data, supports calculation and plotting of some common free recall measures, and has tools for measuring the "memory fingerprint" of individuals.Contributing to PsifrContributions are welcome to suggest new features, add documentation, and identify bugs. See thecontributing guidelinesfor an overview.
Package: psims
psimsPrototype work for a unified API for writing Proteomics Standards Initiative standardized formats
for mass spectrometry:mzMLmzIdentMLmzMLbSee theDocumenationfor more informationInstallationWith pip:pipinstallpsimsWith conda:condainstall-cbioconda-cconda-forge-cdefaultspsimsmzML Minimal Examplefrompsims.mzml.writerimportMzMLWriter# Load the data to writescans=get_scan_data()withMzMLWriter(open("out.mzML",'wb'),close=True)asout:# Add default controlled vocabulariesout.controlled_vocabularies()# Open the run and spectrum list sectionswithout.run(id="my_analysis"):spectrum_count=len(scans)+sum([len(products)for_,productsinscans])without.spectrum_list(count=spectrum_count):forscan,productsinscans:# Write Precursor scanout.write_spectrum(scan.mz_array,scan.intensity_array,id=scan.id,params=["MS1 Spectrum",{"ms level":1},{"total ion current":sum(scan.intensity_array)}])# Write MSn scansforprodinproducts:out.write_spectrum(prod.mz_array,prod.intensity_array,id=prod.id,params=["MSn Spectrum",{"ms level":2},{"total ion current":sum(prod.intensity_array)}],# Include precursor informationprecursor_information={"mz":prod.precursor_mz,"intensity":prod.precursor_intensity,"charge":prod.precursor_charge,"scan_id":prod.precursor_scan_id,"activation":["beam-type collisional dissociation",{"collision energy":25}],"isolation_window":[prod.precursor_mz-1,prod.precursor_mz,prod.precursor_mz+1]})CitingIf you usepsimsin an academic project, please cite:Klein, J. A., & Zaia, J. (2018). psims - A declarative writer for mzML and mzIdentML for Python. Molecular & Cellular Proteomics, mcp.RP118.001070. https://doi.org/10.1074/mcp.RP118.001070
Package: psinspect
psinspectis a visual application to check for CMB power spectra and covariance matrices
calculations throughPSpipe.InstallingThe easiest way is to get the PyPI version withpipinstallpsinspect[--user]You can test if everything has been properly installed withpsinspectIf everything goes fine, no errors will occur. Otherwise, you can report your problem on theIssues tracker.If you plan to develop/change something insidepsinspect, it is better to checkout the latest version by doinggitclonehttps://github.com/xgarrido/psinspect.git/where/to/cloneThen you can install thepsinspectlibrary and its dependenciesviapipinstall-e/where/to/cloneThe-eoption allow the developer to make changes within thepsinspectdirectory without having
to reinstall at every changes.Usingpsinspectat NERSCAt NERSC, after having setpythonwithmodule load python, you can follow the same
installation process and everything will be installed in your local home. Then you can go toNERSC
Jupyter Huband start a notebook with the following minimal set of
commandsfrompsinspectimportAppmy_app=App()my_app.initialize()my_app.start()Another (smarter) way is to encapsulate the whole installation stuff within apythonvirtual
env. to avoid conflicts with your existing installation. To do so you can follow these command linesmoduleloadpythonpython-mvenv/where/to/install/your/envsource/where/to/install/your/env/bin/activatepython-mpipinstallipykernelpython-mipykernelinstall--user--name=psinspectThis will install a new kernel namedpsinspectthat you can choose when you will create a new
notebook inNERSC Jupyter Hub. Copy-paste the abovepythoncode
and execute the cell.The code is part ofPSpipethe Simons Observatory power spectrum pipeline.
Package: psion
Project PsionThis library provides an implementation for asynchronous authentication and authorization of web applications.
It provides support for OAuth 2.1 and OpenID Connect, as well as JOSE.For more details, please visit the documentation.Any doubts and suggestions can be sent to myemail.
Just prepend the title with #Psion#, and I will try my best to answer.JSON Object Signature and Encryption (JOSE)It implements the following RFCs.RFC 7515 - Json Web Signature (JWS)RFC 7516 - Json Web Encryption (JWE)RFC 7517 - Json Web Key and Keyset (JWK)RFC 7518 - Json Web Algorithms (JWA)RFC 7519 - Json Web Token (JWT)For more details on each feature, please visit the respective documentation.OAuth 2.1It implements the following RFCs and Specs.Draft - The OAuth 2.1 Authorization FrameworkRFC 7009 - OAuth 2.0 Token RevocationLicenseThis project is licensed under the MIT License.
Package: psionapp
Thepsionapppackage provides a basis for defining, saving, and retrieving application-level and user-level configuration variables (attributes).The package exposes two classes: ApplicationClass and ApplicationUser.ApplicationClassAn ApplicationClass object will define the application-level configuration (if any).An ApplicationClass object will be identified by anapplication id. You can instantiate a class object with the following parameters:use_user_ini (bool). If True this will create a.inifile in the system appropriateconfigdirectory. Initially a file will be created for the current system user's user name. This will store user-level configuration values.use_config_ini (bool). If True this will create aconfig.inifile in the system appropriateconfigdirectory. This will store application-level configuration values.use_var_dir (bool). If True this will create anapplication iddirectory in the system appropriatevar datadirectory.user_class (ApplicationUser). Used in conjunction withuse_user_ini. The ApplicationUser class will define the config parameters.attributes (seeAttribute definition)ApplicationUserAn ApplicationUser object will define the user-level configuration (if any).An ApplicationUser class will be identified by auser name. It will define the attributes that will form that user's configuration values.Attribute definitionAttibutes must be defined in the form:attributes={'string_data':('general','string','abc'),'int_data':('general','int',2),'bool_data':('display','bool',True),'float_data':('display','float',1.23456),}where theattributesdict key is the attribute name, and the three tuple values are:the section in the config file where the attribute will appear;the data type of the attribute;the default value of the attribute.Version 0.0.5 - 21 January 2021DummyVersion 0.0.4 - 14 August 2020include download pathVersion 0.0.3 - 3 August 2020refactor application last_userVersion 0.0.2 - 2 August 2020remove _applicationbaserefactor user creation and saverefactor applicationclassVersion 0.0.1 - 1 August 2020Added version and _all_Added App level attributesAdded READMEVersion 0.0.0 - 30 July 2020Created and uploaded