Saravanan666/pypi-org huggingface dataset:
Package: sifflet
Sifflet CLIThis package provides a command line interface (CLI) toSiffletapplication.Getting StartedInstallationSifflet CLI is compatible with Python version 3.7 or greaterpipinstallsifflet

sifflet--versionConfigurationBefore using the Sifflet CLI, you need to set your tenant and credentials.
You can do it in several ways:Sifflet command linesifflet configure(which will persists configuration in a file)Or with environment variablesYou will need in both cases the following information:<access_token>: you can find more information on how to generate ithereFor SaaS version:<tenant_name>: Name of your tenant. Sifflet UI URL ishttps://<tenant_name>.siffletdata.com. Sifflet Backend URL ishttps://<tenant_name>api.siffletdata.com. For instance, if you access to Sifflet UI withhttps://mycompany.siffletdata.com, then your tenant would bemycompany.For self-hosted deployment:<backend_url>: Full URL to the Sifflet backend on your deployment including for instance:https://sifflet-backend.mycompany.comSifflet configureYou can input the tenant and credentials directly with thesifflet configurecommand.For SaaS version:>siffletconfigure--tenant<tenant_name>
YourAPIaccesstoken[None]:<access_token>or, for self-hosted deployment:>siffletconfigure--backend-url<backend_url>
YourAPIaccesstoken[None]:<access_token>The use ofsifflet configurewill persist the configuration in a file located at~/.sifflet/config.ini(or in%UserProfile%\.sifflet/config.inion Windows).Environment variablesTo use environment variables, you can do the following:For SaaS version:>exportSIFFLET_TENANT=<tenant_name>
>exportSIFFLET_TOKEN=<access_token>or, for self-hosted deployment:>exportSIFFLET_BACKEND_URL=<backend_url>
>exportSIFFLET_TOKEN=<access_token>You can check that setup is done correctly withsifflet status.>siffletstatus

Siffletversion=x.x.xTenant=my_tenant
Tenantisupandreachable
Tokenexpirationdate=2024-01-0100:00:00
TokenisvalidwithscopeAPIStatus=OKIf you encounter any error at this step, please check your tenant and token configurations.DocumentationThe documentation with the available commands can be foundhere.HelpThe--helpoption is available for any command, for instancesifflet --helporsifflet rules list --help.ExamplesTrigger a specific ruleFirst, find your rule id withsifflet rules list. You can filter with--nameto narrow your search.# Display rulessiffletruleslist
siffletruleslist--name<search_criteria>Then you can trigger one of several rules withsifflet rules run# Run one or many rulessiffletrulesrun--id<rule_id>
siffletrulesrun--id<rule_id_1>--id<rule_id_2>--id<rule_id_3>Finally, retrieve the run status of a rule withsifflet rules run_history# Display rule runs history for a given rule idsiffletrulesrun_history--id<rule_id>Send dbt artifactsIf you have already added the datasource to Sifflet, you can then send the dbt artifacts for syncing the data.# send the dbt artifactssiffletingestdbt--project-name<project_name>--target<target>--input-folder<dbt_artifacts_directory>ContactFor any queries, you can reach us atcontact@siffletdata.com
Package: sifima
===|simplified file management|====English:Program simplified file management can make it easier for you to work with files.info:
print(sifima.name);
print(sifima.version);
print(sifima.author);
print(sifima.info).sifima.create([name_file]) - creates an empty filesifima.write([name_file], [text]) - writes a specific text to a file or creates a file with a specific text.sifima.remove([name_file]) - deletes the filesifima.read([name_file]) - reads the file.that would output the file, it is necessary to make sifima.read into a variabletext = sifima.read([name_file])
Package: si-fi-o
InstallationThis project is published
on thePython Package Index (PyPI)at:https://pypi.org/project/si-fi-o/.
It should be installable from Python distribution platforms or Integrated Development Environments (IDEs).
Otherwise, it can be installed from a command console:For all users, after acquiring administrative rights:First installation:pip installsi-fi-oInstallation update:pip install--upgradesi-fi-oFor the current user (no administrative rights required):First installation:pip install--usersi-fi-oInstallation update:pip install--user--upgradesi-fi-oDocumentationThe documentation is available athttps://src.koda.cnrs.fr/eric.debreuve/si-fi-o/-/wikis/home.AcknowledgmentsThe project is developed withPyCharm Community.The development relies on several open-source packages
(seeinstall_requiresinsetup.py, if present; otherwiseimportstatements should be searched for).The code is formatted byBlack,The Uncompromising Code Formatter.The imports are ordered byisort…your imports, so you don’t have to.
Package: sifr
… and with the sign 0 … any number may be written—FibionacciCount things in various time based windows using in-memory, redis or riak
storage.InstallationInstall the basic package:pip install sifrInstallsifrwith redis dependencies:pip install 'sifr[redis]'Installsifrwith riak dependencies:pip install 'sifr[riak]'Installsifrwith sifrd service dependencies:pip install 'sifr[daemon]'ExamplesUsingsifrwith direct storageimportdatetimeimportredis,riakfromsifr.spanimportYear,Month,Day,Hour,Minute,get_time_spansfromsifr.storageimportMemoryStorage,RedisStorage,RiakStorageredis_client=redis.Redis()redis_store=RedisStorage(redis_client)riak_client=riak.RiakClient()riak_store=RiakStorage(riak_client)memory_store=MemoryStorage()stores=[memory_store,redis_store,riak_store]now=datetime.datetime.now()user_id=1page="index.html"# construct the windows. These are the resolutions that will be tracked.spans=[span(now,["views","user",user_id])forspanin[Year,Month,Day,Hour,Minute]]# incr a counter for all resolutions[store.incr_multi(spans)forstoreinstores]# incr a unique counter[store.incr_unique_multi(spans,page)forstoreinstores][store.incr_unique_multi(spans,page)forstoreinstores]# track the page view[store.track_multi(spans,page)forstoreinstores][store.track_multi(spans,page)forstoreinstores]# get the counts/uniques for a single year windowforstoreinstores:assert1==store.count(Year(now,["views","user",1]))assert1==store.cardinality(Year(now,["views","user",1]))assertset(["index.html"])==store.uniques(Year(now,["views","user",1]))# get the counts/uniques for a rangestart=now-datetime.timedelta(minutes=1)end=now+datetime.timedelta(minutes=1)span_range=get_time_spans(start,end,["views","user",1],[Minute])forstoreinstores:assert[1]==[store.count(span)forspaninspan_range]assert[1]==[store.cardinality(span)forspaninspan_range]assert[set(["index.html"])]==[store.uniques(span)forspaninspan_range]Usingsifrvia rpcsifr.yml (using a redis backend)storage:redisredis_url:redis://localhost:6379/1host:localhostport:6000sifr.yml (using a riak backend)storage:riakriak_nodes:-host:localhostpb_port:8087host:localhostport:6000Run the serversifrdmsgpack_server--config=sifr.ymlInteract with the serverfromsifrimportRPCClientclient=RPCCient(host='localhost',port=6000,resolutions=["year","month","day"])client.incr("views:user:1")client.incr_unique("views:user:1","index.html")client.incr_unique("views:user:1","index.html")client.track("views:user:1","index.html")client.track("views:user:1","index.html")assert1==client.count("views:user:1",datetime.datetime.now(),"day")assert1==client.cardinality("views:user:1",datetime.datetime.now(),"day")assertset(["index.html"])==client.uniques("views:user:1",datetime.datetime.now(),"day")ReferencesMinutemanZeroChangelog0.0.4 2015-06-16Removed transactional pipelines from redis storage.0.0.3 2015-06-10Initial release
Package: sifreader
# SIF ReaderThis package is designed to read Andor SIF image file.In addition to raw image data, it provides access to the wavelength axis for spectra, and metadata such as
exposure time, gain, recording date etc. Image data can be read as anumpyarray or as an
[xarray](https://xarray.pydata.org/en/stable/)DataArray, which contains the image data as well as the coordinate
axes and labels in a single object. For spectra, the spectral axis can be returned as nanometers, wavenumbers or
electron volts.To the best of our knowledge, this is currently the only Python package that can extract wavelength information
from .sif files using only pure Python and numpy (i.e. without relying on any Andor dll libraries).## Requirementsnumpy(optionallyxarray,pytestto run the unit tests)## Installation
Install withpip:` >pip install sifreader `## UsageOpen a file and print the metadata:` >file =SIFFile('my_image.sif')>print(file) <sifreader.sifreader.sifreader.SIFFile object at 0x30f9eecc0> Original Filename:E:\test_sequence.sifDate:                          Thu May 10 12:01:48 2018 ... `Read a single or all frames contained in the file as numpy arrays:` >first_frame = file.read_block(0) >all_frames = file.read_all() `The horizontal axis is contained in thex_axismember variable. If the file contains a spectrum, the axis will be
the wavelength in namometers, otherwise it will contain the pixel numbers:` >wavelengths = file.x_axis `### With the optional xarray packageRead all frames in the file as aDataArray:` >xarr = file.as_xarray() `For spectra: make aDataArraythat contains the photon energy in eV rather than the default wavelength:` >spectrum_file =SIFFile('my_spectrum.sif')>xarr =spectrum_file.as_xarray('photon_energy')`One of the nice features ofDataArrayis the ability to easily select data and plot it:`pythonspectrum_file.as_xarray().sel(frames=0,wavelength=slice(749.5,768.2)).plot()`## Version History0.2: Added support for wavelength information, xarray exporting and unit tests0.1: First release
Package: siftapi
A Python wrapper aroundSift API. Currently
supports Python 2.7.11 and Python 3.5.1.InstallationFrom the repositorypython setup.py installFrom pippip install siftapiUsageimportsiftapisift=siftapi.Sift(API_KEY,API_SECRET)Testingpython setup.py testTest cases are written intestsfolder.Files required for test cases:sensitive.py- File containing your API_SECRET and API_KEY. An example
file,sensitive.py.examplecan be found in the directorytest.eml- A test.emlfile to be sent for parsingDocumentationDocumentation for the SDK can be foundhere.ChangelogChangelog can be foundhere.
Package: sifter
Python implementation of the Sieve email filtering language (RFC 5228).https://github.com/garyp/sifterFEATURESSupports all of the base Sieve spec from RFC 5228, except for features still
listed under TODO belowExtensions supported:regex (draft-ietf-sieve-regex-01)EXAMPLEimport email
import sifter.parser
rules = sifter.parser.parse_file(open('my_rules.sieve'))
msg = email.message_from_file(open('an_email_to_me.eml'))
msg_actions = rules.evaluate(msg)In the above example,msg_actionsis a list of actions to apply to the
email message. Each action is a tuple consisting of the action name and
action-specific arguments. It is up to the caller to manipulate the message and
message store based on the actions returned.WARNINGSNo thought has been given yet to hardening against malicious user input. The
current implementation is aimed at users that are running their own sieve
scripts.The current implementation is not optimized for performance, though hopefully
it’s not too slow for normal inputs.TODOIn rough order of importance:An example adaptor that provides Unix LDA behavior using sieve for filteringBase spec features not yet implemented:encoded characters (section 2.4.2.4)multi-line strings (section 2.4.2)bracketed comments (section 2.3)message uniqueness (section 2.10.3)envelope test (section 5.4)handle message loops (section 10)limit abuse of redirect action (section 10)address test should limit allowed headers to those that contain addresses
(section 5.1)Make sure character sets are actually handled according to the specMake string parsing comply with the grammar in section 8.1 and the features
described in section 2.4.2Check that python’semail.messageimplements header comparisons the same
way as the sieve specMake sure regular expressions are actually handled according to the extension
specAdd support for various extensions:variables (RFC 5229)externally stored lists (draft-melnikov-sieve-external-lists)body (RFC 5173)relational (RFC 5231)subaddress (RFC 5233)copy (RFC 3894)environment (RFC 5183)date and index (RFC 5260)editheader (RFC 5293)ihave (RFC 5463)mailbox metadata (RFC 5490)notifications (RFC 5435), mailto notifications (RFC 5436), xmpp
notifications (RFC 5437)
Package: sifter3
Sifter3 - Sieve email filter (RFC 5228)Sifter3 is a Python 3 implementation of the Sieve email filter language (RFC 5228)FEATURESSupports all of the base Sieve spec from RFC 5228, except for
features still listed under TODO belowmultiline strings (since version 0.2.2)bracketed comments (since version 0.2.4)Extensions supported:regex (draft-ietf-sieve-regex-01)body (RFC 5173)variables (RFC 5229)enotify (RFC 5435, particularly the mailto method RFC 5436)imap4flags (RFC 5232: setflag, addflag, removeflag; not supported: hasflags, :flags)reject and ereject (RFC 5429)ihave (RFC 5463)INSTALLpip install sifter3EXAMPLEimport email
import sifter.parser
rules = sifter.parser.parse_file(open('my_rules.sieve'))
msg = email.message_from_file(open('an_email_to_me.eml'))
msg_actions = rules.evaluate(msg)In the above example,msg_actionsis a list of actions to apply to the
email message. Each action is a tuple consisting of the action name and
action-specific arguments. It is up to the caller to manipulate the
message and message store based on the actions returned.COMMAND LINEThe output of the command line tool can be parsed as json.$ sifter tests/evaluation_1.rules tests/evaluation_1.msg
[['redirect', 'acm@example.com']]WARNINGSNo thought has been given yet to hardening against malicious user
input. The current implementation is aimed at users that are running
their own sieve scripts.The current implementation is not optimized for performance, though
hopefully it's not too slow for normal inputs.TODOAn example adaptor that provides Unix LDA behavior using sieve for
filteringBase spec features not yet implemented:encoded characters (section 2.4.2.4)message uniqueness (section 2.10.3)envelope test (section 5.4)handle message loops (section 10)limit abuse of redirect action (section 10)address test should limit allowed headers to those that contain
addresses (section 5.1)Make sure character sets are actually handled according to the specMake string parsing comply with the grammar in section 8.1 and the
features described in section 2.4.2Check that python'semail.messageimplements header comparisons
the same way as the sieve specMake sure regular expressions are actually handled according to the
extension specAdd support for various extensions:externally stored lists (draft-melnikov-sieve-external-lists)relational (RFC 5231)subaddress (RFC 5233)copy (RFC 3894)environment (RFC 5183)date and index (RFC 5260)editheader (RFC 5293)mailbox metadata (RFC 5490)xmpp notifications (RFC 5437)
Package: sift-sc
Signal FilTering is a tool for uncovering hidden biological processes in single-cell data.
It can be applied to a wide range of tasks, from the removal of unwanted variation as a pre-processing step,
through revealing hidden biological structure by utilizing prior knowledge with respect to existing signal,
to uncovering trajectories of interest using reference data to remove unwanted variation.Visit ourdocumentationfor installation, tutorials, examples and more.ManuscriptPlease see our manuscriptZoe Piran and Mor Nitzan (2022).InstallationInstall SiFT via PyPI by running:pip install sift-sc
Package: sig2srv
sig2srvsig2srv converts incoming signals into suitable service(8) commands.Free software: MIT licenseDocumentation:https://sig2srv.readthedocs.io.FeaturesTurns SIGTERM into “service XYZ stop” commands.Turns SIGHUP into “service XYZ restart” commands.Runs “service XYZ status” periodically and exits with a nonzero status if the
service is no longer seen as running, i.e. the status command returns a
nonzero status.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2017-07-10)First release on PyPI.
Package: sigaa-cli
sigaa-cliMódulo Python (não oficial) para servir como base para o desenvolvimento de um Command Line Interface para a plataforma SIGAA.Com o desenvolvimento de uma interface de linha de comando ou até mesmo uma aplicação com interface gráfica poderemos assim utilizar o SIGAA com redução, em um certo grau, dos problemas gerados por uma internet de baixa qualidade ou de inconsistências na compatibilidade com navegadores.DocumentationThe documentation ishereInstallationRun the following to install:pipinstallsigaa-cliFuncionalidades Previstas:Observação: Novas funcionalidades podem ser solicitadas através da abertura de umanova issueou de umpull request.Autenticação com a plataforma onlineGerar um id de sessão não autencicado;Autenticar o id de sessão gerado anteriormente;Desautenticar um id de sessão gerado anteriormente;Acessar Cookies da sessão;Verificar status da authenticação do sistema;Extrair info sobre os usuários da plataforma.Pegar uma lista com todos os nomes completos e nome de usuários da plataforma;[] Pesquisar os nomes completos de usuários pelo seu username.Listar TarefasA partir do código de uma turma ativa do estudante, poder gerar uma lista de tarefas daquela determinada turma, extraindo informações referente a prazos.Mais funcionalidades serão adicionadas a medida que as já propostas forem entregues.
Package: sigal
Sigal is yet another simple static gallery generator. It’s written in Python
and it allows to build a static gallery of images with the following features:Process directories recursively.Generate HTML pages usingJinja2templates.Relative links for a portable output.Support themes, videos, EXIF tags, zip download.Parallel processing.MIT licensed.The idea behind Sigal is to ease the use of the javascript libraries likegalleria. These libraries do a great job to display the images, Sigal does
what is missing: resize images, create thumbnails, generate HTML pages.Sigal requires Python 3.8+.Links :Latest documentation on thewebsiteSource, issues and pull requests onGitHubReleases onPyPI#sigalon Freenode, or with thewebchatinterface.Themes & DemoSigal comes with three themes, based on thecolorbox,galleriaandphotoswipeJavascript libraries:colorbox demogalleria demophotoswipe demo
Package: siganalysis
Python (2.7+/3.4+) routines for analyzing signals. Some of the functions
include:CalculatingShort-Time Fourier
TransformSmoothing a signalPlotting an STFT’s spectrogramCalculating the peak hold of an STFT in the freq domainPlotting the peak hold of an STFTThe above functions are handy when analyzing signals recorded in the
time domain, such as using a TEAC LX-10 data recorder, and seeing the
frequency spectrum. This is useful for Electromagnetic Compatibility
(EMC) analyses.InstallationYou can installsiganalysiseither via
the Python Package Index (PyPI) or from source.To install using pip:$pipinstallsiganalysisSource:https://github.com/questrail/siganalysisRequirementssiganalysisrequires the
following Python packages:numpyscipymatplotlibContributingsiganalysisis developed
usingScott Chacon’sGitHub
Flow. To
contribute, forksiganalysis, create a
feature branch, and then submit a pull request.GitHub
Flowis
summarized as:Anything in themasterbranch is deployableTo work on something new, create a descriptively named branch off ofmaster(e.g.,new-oauth2-scopes)Commit to that branch locally and regularly push your work to the
same named branch on the serverWhen you need feedback or help, or you think the branch is ready for
merging, open apull
request.After someone else has reviewed and signed off on the feature, you
can merge it into master.Once it is merged and pushed tomaster, you can andshoulddeploy immediately.Running the Testssiganalysishas been
tested against Python 2.7 and Python 3.4. Whilesiganalysismay work with
other versions of Python, it hasn’t been tested with anything else at
this time. Assuming you havevirtualenvandvirtualenvwrapperinstalled, you can run the following commands to clone the GitHub
repository, create a virtualenv, and run the unit tests:$gitclonehttps://github.com/questrail/siganalysis.git$cdsiganalysis$mkvirtualenvsiganalysis$pipinstall-rrequirements.txt$nosetestsLicensesiganalysisis released
under the MIT license. Please see theLICENSE.txtfile for more information.
Package: sigc
sigcinstallpipinstallsigccore usageimportsigckegg_metabolism=sigc.metabolism_sigs(resources='KEGG')# other custom signature gene sets also supportdf=pd.read_table("cells_X_genes.mat",header=0,index_col=0)sig_mtx=sigc.sigc_score(df,kegg_metabolism,method="AUCell")# sig_mtx: cells_X_signaturessignature examplenamedescriptionmemberGlycolysis00010HK3Glycolysis00010HK1Glycolysis2describ2geneXDocumentationExtensive documentation and tutorials are available athttps://sigc.readthedocs.io
Package: sig-decomp
OSD: Optimization(-based) Signal DecompositionModeling language for finding signal decompositionsThis software provides a modeling language for describing and solving signal decomposition problems. This framework is described in detail in an acompanyingmonograph. Examples are available in thenotebooksdirectory.Brief overviewWe formulate the problem of decomposing a signal into components as an optimization problem, where components are described by their loss functions. Once the component class loss functions are chosen, we minimize the total loss subject to replicating the given signal with the components. Our software provides a robust algorithm for carying out this decomposition, which is guaranteed to find the globally optimal descomposition when the loss functions are all convex, and is a good heuristic when they are not.Vector times series signals with missing entriesWe consider a vector time series or signal,y, which possibly has missing entries. We represent the signal compactly as aT x pmatrix, withTtime steps andpentries at each time. Some of these entries may be missing—filled withNaNor some other missing value indicator. We will be concerned with finding decompositions that exactly match the data at the known entries.Signal decompositionWe model the given signalyas a sum (or decomposition) ofKcomponent,x^1,...,x^K. Each componentx^kis also aT x pmatrix, but they do not have any missing values. Indeed, we can use the values ofx^1,...,x^Kas estimates of the missing values in the original signal. This is useful for data imputation as well as model validation.Component classesTheKcomponents are characterized by cost functions that encode the loss of or impolausibility that a component takes on a certaint value. (In some cases, we can interpret the classes statistically, with the cost function corresponding to the negative log-likelihood of some probability density function, but this is not necessary.) Our solution method is based on evaluating themasked proximal operatorsof the class cost functions. These operators have been defined for many useful classes in theclassesmodule.InstallationWe do not yet have a package released for this code, so for now, please clone the repository and set up a virtual environment with the packages listed in therequirements file.
Package: sigdis
sigdisSignal dispatcherTable of ContentsInstallationLicenseUsageInstallationpip install sigdisLicensesigdisis distributed under the terms of theMITlicense.UsagefromsigdisimportSignalapp_started=Signal()@app_started.connectdefdo_some_actions(data,**_):print("DATA:",data)app_started.send(data="test")Check outtestsfor more.