Saravanan666/pypi-org huggingface dataset:
Package: psquared-client
psquared_clientprojectThepsquared_clientproject contains both thepsquared_clientpackage that
provides a python interface to a PSqaured server, and thepp-clicommand line
interface that uses that package to all copmmand line access to the server.pp-cliexecutableMore details about thepp-cliexecutable can be found using its help optionpp-cli -hbut here are some examples of retrieving information.
(Note:unless  you are using the default local PSquared server you will need to the the environmental
variablePP_APPLICATIONto point the server you want to use.)To list the currently active configurationspp-cliTo list the known versions of a given configurationpp-cli -i <configuration>To display the current state of one or more items for a configuration/version.pp-cli -i -V <version> <configuration> item ...To display the history of one or more items for a configuration/version.pp-cli -H -V <version> <configuration> item ...As an alternate, the list of items to be acted upon can be supplied in a file with one item per line.
Thus the following displays the current state of a set of items listed in a file for a configuration/version.pp-cli -i -f <file> -V <version> <configuration>Here are some examples of commands that affect the state of PSquared.
(Note:you may need an authorized certificate to execute these types of commands.)To submit one or more items for processing with a configuration/version.pp-cli -i -s -V <version> <configuration> item ...To resolve the failure of one or more items for a configuration/version.pp-cli -i --resolve -V <version> <configuration> item ...The other transitions,submit,cancel,reset, 'abandonandrecover` have similar options.psquared_clientpackageThepsquared_clientpackage provides thePSquaredclass that can be used to
access a PSqaured server and aDisplaymodule that can display the responses
of a PSquared server in a readable format.PSquaredclassThePSquaredclass provides various documents in reponse to a request to the
PSquared server. The documents are in the form of a standard pythonElementTree. Currently the following methods are supported.get_application- returns the application document at the URLget_configuration- returns the configuration document the named configurationget_report- returns the requested report documentexecute_submissions- submits a list of items for processingexecute_transitions- execute a transition for a list of itemsDisplaymoduleTheDisplaymodules provides various methods for displaying the reponses that
result from a request to the PSquared server. Currently the following methods
are supported.configurations- displays all the configurations contained in an application documentversions- displays all the versions of a named configurationentry- displays the state of a given iteminfo- displays the states of a set of items contained in a reporthistories- displays the history of one or more items contained in history document
Package: psquery
This package allows you to query Turi Predictive Services.Refer tohttps://turi.com/products/predictive-services/docs/userguide/connecting.html#psqueryfor detailed information regarding how to use this client.This package only includes theconfig.pyandquery_client.pyfrom the full Predictive Services client psclient.ConnectingIn order to connect, you need to know the query endpoint URL and your key. An
API key or an admin key is sufficient to query the Predictive Services. An API
key may have limitations on which endpoints it can query. Consult with your
system administrator to obtain the appropriate key.Thepsquery.connectfunction is used to connect to the service.:import psquery
conn = psquery.connect(query_endpoint_URL, key_id)Please consult theconnect function documentationfor details on alternative methods of specifying these parameters during connection.QueryingOnce you have connected, you can query by specifying the endpoint you want to
query and the parameters. Keep in mind that your key must have sufficient
privileges to access the endpoint. Admin keys can access all endpoints, but
API keys must be specifically allowed by endpoint name.:response = conn.query('add', 1, 2)The response is a dict with the following keys and values.:{u'node': u'...', u'uuid': u'...', u'version': 1, u'from_cache': False, u'model': u'add', u'response': 3}Please see thequery method documentationfor more details.FeedbackIf the endpoint allows it, you can specifyfeedback::conn.feedback(response['uuid'], success=True)Please see thefeedback method documentationfor more details.
Package: psrc
psrcDeveloper GuideSetup# create conda environment$mambaenvcreate-fenv.yml# update conda environment$mambaenvupdate-npsrc--fileenv.ymlInstallpipinstall-e.# install from pypipipinstallpsrcnbdev# activate conda environment$condaactivatepsrc# make sure the psrc package is installed in development mode$pipinstall-e.# make changes under nbs/ directory# ...# compile to have changes apply to the psrc 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:$pipinstallgit+https://github.com/dsm-72/psrc.gitor fromconda$condainstall-cdsm-72psrcor frompypi$pipinstallpsrcDocumentationDocumentation can be found hosted on GitHubrepositorypages. Additionally you can find
package manager specific guidelines oncondaandpypirespectively.
Package: psrcal
Proper Scoring Rules for calibration.This repository contains the code for doing calibration on multi-class and binary classification using various approaches. The core functionality was written by Niko Brummer. Sergio Alvarez and I later added various methods and scripts to experiment with them. Further, I have another repository calledexpected_costwith lots of examples on how to use the libraries in this repository.How to installpip install psrcalWhen you do that, torch, matplotlib and other libraries will also be installed, unless you already have the required versions in your system. It does not install joblib and ternary, which are only needed to run the scripts in the experiments dir. If you want to run those scripts, you can install those two packages separately.Alternatively, if you want the latest version of the code, you can:Clone the repository:git clone https://github.com/luferrer/psr-calibration.gitInstall the requirements (this does install joblib and ternary since it assumes you are probably cloning the repo in order to run the scripts inside the experiments dir):pip install -r requirements.txtAdd the resulting top directory in your PYTHONPATH. In bash this would be:export PYTHONPATH=ROOT_DIR/psr-calibration:$PYTHONPATHwhere ROOT_DIR is the absolute path (or the relative path from the directory where you have the scripts or notebooks you want to run) to the top directory from where you did the clone above.
Package: psrecord
Aboutpsrecordis a small utility that uses thepsutillibrary to record the CPU
and memory activity of a process. The package is still under development
and is therefore experimental.The code is released under a Simplified BSD License, which is given in
theLICENSEfile.RequirementsPython 2.7 or 3.3 and higherpsutil1.0 or latermatplotlib(optional, used for
plotting)InstallationTo install, simply do:pip install psrecordUsageBasicsTo record the CPU and memory activity of an existing process to a file (use sudo for a root process):psrecord 1330 --log activity.txtwhere1330is an example of a process ID which you can find withpsortop. You can also usepsrecordto start up a process
by specifying the command in quotes:psrecord "hyperion model.rtin model.rtout" --log activity.txtPlottingTo make a plot of the activity:psrecord 1330 --plot plot.pngThis will produce a plot such as:You can combine these options to write the activity to a file and make a
plot at the same time:psrecord 1330 --log activity.txt --plot plot.pngDuration and intervalsBy default, the monitoring will continue until the process is stopped.
You can also specify a maximum duration in seconds:psrecord 1330 --log activity.txt --duration 10Finally, the process is polled as often as possible by default, but it
is possible to set the time between samples in seconds:psrecord 1330 --log activity.txt --interval 2SubprocessesTo include sub-processes in the CPU and memory stats, use:psrecord 1330 --log activity.txt --include-childrenRunning testsTo run tests, you will needpytest. You can install it with:pip install pytestYou can then run the tests with:pytest psrecordReporting issuesPlease report any issues in theissue
tracker.
Package: ps.releaser
ps.releaserps.releaserprovides several plugins forzest.releaser.
The plugins are registered globally and can be activated if needed.Available PluginsExportig Diazo Themes as ZIP filesPlone allows us to upload diazo themes as zip files.
This can be used when we don’t have the permission to install our theme on the server as a python package (e.g. within a shared hosting environment).ps.releaserprovides a hook which is run after the release has been done.
Use the following options in yoursetup.cfgto enable the ZIP file export:[ps.releaser]
diazo_export.enabled = 1
diazo_export.path = src/my/package/diazo_resources
diazo_export.adjust_title = 1diazo_export.enabledActivate or deactivate the export.diazo_export.pathPath relative from the package root to the folder containing the diazo resource files.diazo_export.adjust_titleAppend the version number of the package to the title in the zippedmanifest.cfgfile.InstallationUse in a buildout[buildout]
parts += releaser

[releaser]
recipe = z3c.recipe.scripts
dependent-scripts = true
eggs =
    ps.releaser
    my.packageIf you want to use the latest development version from GitHub, addps.releaserto yourmr.developersource section:[buildout]
extensions += mr.developer

[sources]
ps.releaser = git git@github.com:propertyshelf/ps.releaser.gitThis creates thezest.releaserexecutables in your bin-directory.
Create a release as you’re used to:$ ./bin/fullreleaseInstallation in a virtualenvYou can also installps.releaserin a virtualenv.:$ pip install ps.releaserYou can also install the latest version ofps.releaserdirectly from GitHub:$ pip install -e git@github.com:propertyshelf/ps.releaser.git#egg=ps.releaserNow you can use it like this (when releasing your package):$ fullreleaseChangelog0.3 (2016-02-08)Added console script to only release diazo theme as zip.0.2 (2015-03-03)Use thediazo_exportprefix for settings related to diazo zip file export.0.1 (2015-03-02)Initial release.
Package: psr-formats
psr-formatsFormats for loading and saving data used in pulsar data processing.Supported formats:DADAUsage>>>frompsr_formatsimportDADAFile>>>dada_file=DADAFile("path/to/dada.dump").load_data()>>>dada_file["NCHAN"]'1'>>>dada_file.nchan1>>>dada_file.npol2>>>dada_file.sampling_rate<Quantity0.025us>>>>dada_file.data.shape(3107730,1,2)# ndat, nchan, npol>>>new_dada_file=DADAFile("new.dump")>>>new_dada_file.data=dada_file.data>>>new_dada_file.dump_data()'new.dump'Testingpoetry run python -m unittest
Package: psrmodels
psrmodels: a package for adequacy of supply analysis in power systemsThis packages implements some useful functions to do adequacy of supply analysis in single area and 2-area power systems. The focus is on time-collapsed models, but efficient simulation functionality is also implemented for time-sequential analysis. In the case of time-collapse models, some semi-parametric extreme value models are available for analysing both net demand (demand minus renewables) and power margins.InstallationThe package is on PyPi, sopip install psrmodelsis enough to install the package. It runs on Python>=3.6UsageYou can see the full documentationhere. Below some toy models are created for time-collapsed and time-sequential analysistoy bivariate time-collapsed and time-sequential modelsimportnumpyasnpimportpandasaspdfrompsrmodels.time_collapsedimportBivariateHindcastMarginastc_modelfrompsrmodels.time_collapsedimportConvGenDistributionastc_convgenfrompsrmodels.time_dependentimportBivariateHindcastMarginastd_modelfrompsrmodels.time_dependentimportConvGenDistributionastd_convgen# create toy demand and wind data with 100 observationsnp.random.seed(1)demand=np.random.normal(loc=1000,scale=50,size=(100,2))wind=np.random.normal(loc=250,scale=50,size=(100,2))# create toy generator datagens=pd.DataFrame({"Capacity":50*np.ones(15),"Availability":0.95*np.ones(15)})# create 2 (identical) conventional generation distributions from generator dataconvgen_dists=[tc_convgen(gens),tc_convgen(gens)]# create time-collapsed bivariate hindcast modelmodel1=tc_model(demand=demand,renewables=wind,gen_dists=convgen_dists)## compute LOLE for area 1 under a 'veto' policy an 1000MW interconnection capacitymodel1.lole(c=1000,policy="veto",axis=0)# now, create a time-sequential model# first, create time-sequential generators. We need to add a 'TTR' (time to repair) column to our generator datagens["TTR"]=50#50 hours to repair on averagetd_convgen_dists=[td_convgen(gens),td_convgen(gens)]# create time-sequential modelmodel2=td_model(demand=demand,renewables=wind,gen_dists=td_convgen_dists)# simulate post-interconnection sequential power margin values under a veto policy and 1000MW interconnectionsim_data=model2.simulate_post_itc(n_sim=1000,c=1000,policy="veto")
Package: psrpcore
psrpcore - Python PowerShell Remoting Protocol Core LibraryCore library for PowerShell Remoting Protocol (PSRP) in Python.
This library enables you to write either client or a server side implementation for PSRP.
It does not provide any IO or concurrency logic as it's designed to be a pure Python implementation that is then used by other library.
This follows thesans-IOparadigm to promote re-usability and have it focus purely on the protocol logic.DocumentationDocumentation is available athttps://psrpcore.readthedocs.io/.RequirementsCPython 3.7+cryptographyInstallFrom PyPIpipinstallpsrpcoreFrom Sourcegitclonehttps://github.com/jborean93/psrpcore.gitcdpsrpcore
pipinstall-e.
Package: psrsigsim
PsrSigSimThe NANOGrav Pulsar Signal SimulatorFree software: MIT licenseDocumentation:https://psrsigsim.readthedocs.io.Image Credit: Caitlin WittThe Pulsar Signal Simulator (PsrSigSim) is a Python package developed by the
North American Nanohertz Observatory for Gravitational Waves (NANOGrav). This
software is free to use and is designed as a tool for simulating realistic
pulsar signals, as well as educating students and researchers about those
signals. Various models from the copious pulsar literature are used for
simulating emission, propagation effects and data processing artifacts.GoalsInvestigate sources of time-of-arrival errors:Simulate various sources of time of arrival errors, including interstellar medium effects, intrinsic pulsar noise, various pulsar emission mechanisms and gravitational waves. Simulate instrumental noise sources, including radio frequency interference, radiometer noise and backend sampling effects.Education and Outreach:Allow users to build pulsar signals piece-by-piece to demonstrate to students how pulsar signals change as they propagate and how they are changed by the signal processing done at the telescope. Make materials for talks and outreach including signal diagrams and sound files of sonified pulsars.Search algorithms and search training:Test new search algorithms on signals with known parameters. Use simulated signals for search training. Assess the sensitivity of current search algorithms with simulated signals.Code of ConductThePsrSigSimcommunity expects contributors to follow aCode of Conductoutlined with our documentation.CreditsDevelopment Team: Jeffrey S. Hazboun, Brent Shapiro-Albert, Paul T. BakerThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History1.0.1 (2021-02-08)Various changes to documentation and metadata coinciding with JOSS review.1.0.0 (2020-10-15)First release, coinciding with chromatic timing parameter paper.0.2.0 (2020-08-17)Pre Release version for testing.0.1.0 (2018-01-16)*First release on PyPI.
Package: psrt
PSRT Python connectorPython connector forPSRTSDK Documentation:https://psrt-py.readthedocs.io/Client usage example:frompsrtimportClientimporttimedefprocess_message(client,userdata,message):print(message.topic)print(message.payload)client=Client(path='localhost:2873')# client.tls = True# client.tls_ca = '/opt/workstuff/psrt/certs/ca.crt'# client.need_data_socket = Falseclient.on_message=process_messageclient.connect()client.subscribe('test')client.subscribe_bulk(['test2','#'])client.unsubscribe('test')client.unsubscribe_bulk(['test2','#'])client.subscribe('#')for_inrange(3):client.publish('unit/tests/test1','hello')time.sleep(1)client.bye()
Package: psrv
psrvSimple web utility to serve up a system’s statistics using psutil.  This
can be referred to as ps service or process and service remote viewer.
This is a basic demo proof of concept that really aims to showcase how
Python backends and JavaScript frontends can be integrated together.This demo may be installed using the following commands into a Python
virtual environment:$ git clone https://github.com/metatoaster/psrv.git
$ cd psrv
$ pip install -e .[dev,webpack,scss]
$ calmjs npm --install -i -D psrv[dev,webpack,scss]
$ python setup.py build
$ psrv-demoConnect a web browser tohttp://localhost:8000and a simple page showing
the current CPU and memory utilization for the current machine will be
visible with updates done by polling the status endpoint every three
seconds (defined in the JavaScript code in this module).0.0.0 (Unreleased)Initial release.
Package: pss
Introduction: what is pss?pssis a power-tool for searching inside source code files.psssearches recursively within a directory tree, knows which extensions and
file names to search and which to ignore, automatically skips directories
you wouldn’t want to search in (for example.gitor.svn), colors
its output in a helpful way, and does much more.If you’re familiar with theacktool, then you will findpssvery
similar (seehttps://github.com/eliben/pss/wiki/pss-and-ack).Pre-requisitespssneeds only Python to run. It’s being tested with several of the latest
Python 3.x versions on Linux, Mac OS and Windows. Earlier versions of Python 3.x
may work as well, but this isn’t guaranteed. Python 2 is no longer supported
(but you may download pss version 1.43 or older if you need it to work with
Python 2).Installingpsscan be installed from PyPI (Python Package Index):> pip install pssAlternatively, you can download the source distribution either from PyPI or
from the main Github project page. When you unzip the source distribution, run:> python setup.py installRunning without installingpsssupports direct invocation even without installing it. This may
be useful if you’re on a machine without administrator rights, or want to
experiment with a source distribution ofpss.Just unzip thepssdistribution into some directory. Let’s assume its full
path is/path/to/pss. You can now run:> /path/to/python /path/to/pssAnd this will invokepssas expected. This command can also be tied to an
alias or placed in a shell (or batch) script for convenience.How to use it?pssis meant to be executed from the command line. Running it with no
arguments or with-hwill print a detailed usage message.For some detailed usage examples, check out the
Usage wiki page -https://github.com/eliben/pss/wiki/Usage-samplesLicensepssis open-source software. Its code is in the public domain. See theLICENSEfile for more details.
Package: pssapi
Wrapper for the Pixel Starships APIThis library allows you to request the Pixel Starships API through a Python Wrapper.Supported PlatformsPython: 3.11UsageInstallationYou can install this module using your package management method. For example:pipinstallpssapiGet startedimportasynciofrompssapiimportPssApiClientasyncdefmain():client=PssApiClient()item_designs=awaitclient.item_service.list_item_designs()print(f"Found{len(item_designs)}item designs.")print(f"First item:{item_designs[0].item_design_name}")if__name__=="__main__":loop=asyncio.new_event_loop()asyncio.set_event_loop(loop)asyncio.run(main())ContributingInstall development dependenciesLinux:makeinitWindows:./wininitFormat codeLinux:makeformatWindows:./winformatCheck & TestLinux:makecheck
maketestWindows:./wincheck
./wintestShowcasepoetryrunpythonexamples/showcase.py
Package: ps-signal
Picoscope Signal AnalyzerModule used to import .csv files made form a pico scope and analyse the data.How to installClone repository$ git clone git@github.com:golgor/ps_signal.gitNavigate to the cloned repo$ cd ps_signalInstall using pip$ pip install .How to useThis is a script using a Command Line Interface (CLI). Either you can run it by:$ python -m ps_signalor$ ps_signalMandatory argumentsfile - The path to a file containing data that have been exported to a .csv from the software Picoscope. The file have to be in the following format:Time;Channel A
(ms);(mV)

-200,00015929;-0,21362960
-200,00004729;-0,73854790Arguments-h, --help - Showing a help message with all the available arguments.-i lower upper - Set an interval in the x-axis (time). This can be used to isolate parts of a signal that is of interest.-fff - Used to invoke running a FFT on the given signal.-lp cutoff - Applying a low pass filter on the signal. Can be used to remove high frequency disturbances.-hp cutoff - Applying a high pass filter on the singal. Can be used to remove low frequency disturbances.-bs lower upper - Applying a band stop filter on the signal. Can be used to remove disturbances that is defined by a band in the frequency spectrum.-o - Can be used to set an alternative output folder.--version - Prints the current version of the package.Data filesThis module will work with .csv files as exported from PicoScope 6.14.x.OtherRequirementsUsing pipreqs to generate requirements.txtDocstringsDocstrings are following sphinx format according to:https://sphinx-rtd-tutorial.readthedocs.io/en/latest/docstrings.htmlInstallation?https://github.com/pyinstaller/pyinstaller
Package: pss-pywaapi
pss_waapiHelper package for interfacing with Wwise using waapi.UsageImport the library...
"import pss_pywaapi"Connect to Wwise and get the connection info...
"result = pss_pywaapi.connect()"Default port is 8080, Optionally pass in an alternative port for the connection ...
"result = pss_pywaapi.connect(8080)"Documentation:https://pss-pywaapi.readthedocs.io/
Package: pssq
Builds PostgreSQL queries for execution in more useful Pythonic way. Not ORM.>>> from pssq import Q
>>> Q.select().from_("foo").where(a=5).end()
('SELECT * from "foo" where "a"=$1', (5,))

>>> Q.insert("foo").set(a=5, b=7).end()
('INSERT INTO "foo" ("a", "b") VALUES ($1, $2)', (5, 7))

>>> Q.insert("foo").set(q=Q.unsafe("4"), w=Q.unsafe("now()"), a=5).end()
('INSERT INTO "foo" ("q", "w", "a") VALUES (4, now(), $1)', (5,))Installpip3 install pssqMotivationOriginally written for asyncpg service without ORM. Because writing bind variables for raw PostgreSQL is hard.Testspip3 install pytest
PYTHONPATH=src py.test -s tests