Saravanan666/pypi-org huggingface dataset:
Package: logan-libdyson
Dyson Python LibraryThis library is still under development.This library is a refactor oflibpurecool. There is going to be some major changes:Initializing local connections no longer requires cloud login. Users can use the MQTT credentials directly.The interface of the library will be improved so that the same operation of different types of devices (e.g. PureCoolLink and PureCool) will have the same interface.Zeroconf based auto connection will be decoupled from device classes so that we can use only one zeroconf service to to auto connection for more than one device.The main purpose of this library is to support the HomeAssistant Dyson integration so that it'll only support Python 3.7+ and there is no plan for older versions of Python.
Package: loganmatic
para usar como pacote PIP so usarpip install loganmatice para importar utilizefrom loganmatic import CalculadoradepoisMatematica = Calculadora()depois somente utilizarprint(Matematica.NumeroEuler)print(Matematica.fatorar(100))
print(Matematica.Pi)
print(Matematica.modulo(-4))
print(Matematica.fatorial(4))
print(Matematica.raiz_de_primeiro_grau(2, 1))
print(Matematica.raizDeSegundoGrau(1,2,-3))
print(Matematica.raizCubica(8))
print(Matematica.raizQuadrada(9))
print(Matematica.numero_aleatorio_entre(10, 20))Essas duas ultimas estão com problema para valores float...
print(Matematica.cosseno(0))
print(Matematica.seno(0))retorna:2.7182818284590455 [2, 2, 5, 5] 3.141592653589793 4 24 -0.5 [1.0, -3.0] 2.0 3.0 11 1.0 0A biblioteca calcula (atualmente) o valor de PI e do Numero de Euler ||
Calcula Funções: Seno, Cosseno ||
fatora um numero ||
calcula o fatorial de um numero ||
o modulo ||
gera um numero aleatorio entre 2 numeros ||
calcula raizes cubicas e quadrada e calcula as raizes de equações de primeiro e segundo grauFeito porGabriel Lima
Package: loganom
loganomis a tool that checks mail log files in search of anomalies.The first processor check postfix sasl authentication.Usually, when an account is compromised, attackers use several machines from
their botnets to send emails (spam/virus/phishing/etc) with that credential.This can be observed when a user who usually connects from the same country
suddenly sends emails from worldwide.This is anexperimentalprogram.InstallationInstall using pip:pipinstallloganomSources are available onhttps://github.com/dbaio/loganom.Documentation available onhttps://loganom.readthedocs.io/en/latest/.
Package: log-anomaly-detector
Log Anomaly DetectorLog anomaly detector is an open source project code named "Project Scorpio". LAD is also used for short. It can connect to streaming sources and produce predictions of abnormal log lines. Internally it uses unsupervised machine learning. We incorporate a number of machine learning models to achieve this result. In addition it includes a human in the loop feedback system.Project backgroundThe original goal for this project was to develop an automated means of notifying users when problems occur with their applications based on the information contained in their application logs. Unfortunately logs are full of messages that contain warnings or even errors that are safe to ignore, so simple “find-keyword” methods are insufficient . In addition, the number of logs are increasing constantly and no human will, or can, monitor them all. In short, our original aim was to employ natural language processing tools for text encoding and machine learning methods for automated anomaly detection, in an effort to construct a tool that could help developers perform root cause analysis more quickly on failing applications by highlighting the logs most likely to provide insight into the problem or to generate an alert if an application starts to produce a high frequency of anomalous logs.ComponentsIt currently contains the following components:LAD-Core: Contains custom code to train model and predict if a log line is an anomaly. We are currently use W2V (word 2 vec) and SOM (self organizing map) with unsupervised machine learning. We are planning to add more models.Metrics: To monitor this system in production we utilize grafana and prometheus to visualize the health of this machine learning system.Fact-Store: In addition we have a metadata registry for tracking feedback from false_positives in the machine learning system and to providing a method for ML to self correcting false predictions called the “fact-store”.InstallingInstall Log Anomaly Detector (LAD):pip install -i https://test.pypi.org/simple/ scorpioLAD requires python 3.6 or greaterDocumentationOfficial documentation for LAD can be found athttps://log-anomaly-detector.readthedocs.io/en/latestCommunityFor help or questions about Log Anomaly Detector usage (e.g. "how do I do X?") then you can open an issue and mark it as question. One of our engineers would be glad to answer.To report a bug, file a documentation issue, or submit a feature request, please open a GitHub issue.For release announcements and other discussions, please subscribe to our mailing list (https://groups.google.com/forum/#!members/aiops)Major updates will be presented at our  AiOps special interest group meeting which is a part of openshift commonsOpenShift Commons AiOps Sig Calendar:https://bit.ly/2lMn6yUContributingWe happily welcome contributions to LAD. Please see our contribution guide for details.
Package: logargparser
logargparserUtility for the common case of managing logs from the command line in simple utilities.Sample usageIn a python script calledsample.py:importloggingfromlogargparserimportLoggingArgumentParserlogger=logging.getLogger(__name__)defmain():# Instead of the normal `parser = argparse.ArgumentParser.parser=LoggingArgumentParser(logger)# Add as many other arguments as you want.parser.add_argument('-o',"--output")args=parser.parse_args()logger.info("The logger was set up for us based on --log and/or --logfile command line args.")ifargs.outputisnotNone:# Do something....passif__name__=="__main__":main()We can then invoke the script from the command line.
First, to get help on all args:python-msample-hThe output will be:usage: sample.py [-h] [--log {DEBUG,INFO,WARNING,ERROR,CRITICAL}]
                 [--logfile LOGFILE] [-o OUTPUT]

options:
  -h, --help            show this help message and exit
  --log {DEBUG,INFO,WARNING,ERROR,CRITICAL}
                        Logging level.
  --logfile LOGFILE     Optional file path that logs should be appended to.
                        The file will be created if it does not exist.
  -o OUTPUT, --output OUTPUTNotice that everything worked like a normal script usingargparse.ArgParser, but in addition to the-o/--outputargument we created ourselves, there are two other
arguments--logand--logfileavailable.We can use--logas follows:python-msample--logINFOThis will produce the outputINFO:__main__:The logger was set up for us based on --log and/or --logfile command line args.Notice that this output appears on the standard error
output. It is generated by thelogger.infostatement
in the python code above.If we want this output to be appended to a log file
instead of being written to standard error, we can runpython-msample--logINFO--logfilemylog.logNow when we run it the output will go to the file we
specifcied, just as the help said it would.
Package: logatta-countries
# django-countriesdjango-countries is a Django app to  that provides sessional features to your Django project.Detailed documentation is in the “docs” directory.Quick startAdd “polls” to your INSTALLED_APPS setting like this:INSTALLED_APPS = [
    ...,
    "countries",
]Include the polls URLconf in your project urls.py like this:path("countries/", include("countries.urls")),Runpython manage.py migrateto create the polls models.Runpython manage.py upload_datato upload the data to the database.Start the development server and visithttp://127.0.0.1:8000/admin/to create a poll (you’ll need the Admin app enabled).Visithttp://127.0.0.1:8000/polls/to participate in the poll7. to import MyResponse ,get_path , etc…
usefrom countries.utils import *from countries.utils import MyResponse ,get_path ,etc...
Package: logaut
logautLOGics formalisms to AUTomataWhat islogautLogaut is to the logics-to-DFA problem
what Keras is for Deep Learning:
a wrapper to performant back-ends,
but with human-friendly APIs.InstallTo install the package from PyPI:pip install logautMake sure to haveLydiainstalled on your machine.
We suggest the following setup:Install DockerDownload the Lydia Docker image:docker pull whitemech/lydia:latestMake the Docker image executable under the namelydia.
On Linux and MacOS machines, the following commands should work:echo '#!/usr/bin/env sh' > lydia
echo 'docker run -v$(pwd):/home/default whitemech/lydia lydia "$@"' >> lydia
sudo chmod u+x lydia
sudo mv lydia /usr/local/bin/This will install an alias to the inline Docker image execution
in your system PATH. Instead of/usr/local/bin/you may use another path which is still in thePATHvariable.On Windows, make a.batfile:docker run --name lydia -v"%cd%":/home/default whitemech/lydia lydia %*And add it to your PATH variable.QuickstartNow you are ready to go:fromlogautimportltl2dfafrompylogics.parsersimportparse_ltlformula=parse_ltl("F(a)")dfa=ltl2dfa(formula,backend="lydia")The functionltl2dfatakes in input apylogicsformulaand gives in output
apythomataDFA.Then, you can manipulate the DFA as done with Pythomata,
e.g. to print:dfa.to_graphviz().render("eventually.dfa")Currently, thelydiabackend only supports
theltlandldllogics.Theltlf2dfa, based onLTLf2DFA,
supportsltlandpltl.
First, install it at version1.0.1:pip install git+https://github.com/whitemech/LTLf2DFA.git@develop#egg=ltlf2dfaThen, you can use:fromlogautimportpltl2dfafrompylogics.parsersimportparse_pltlformula=parse_pltl("a S b")dfa=pltl2dfa(formula,backend="ltlf2dfa")Write your own backendYou can write your back-end by implementing
theBackendinterface:fromlogaut.backends.baseimportBackendclassMyBackend(Backend):defltl2dfa(self,formula:Formula)->DFA:"""From LTL to DFA."""defldl2dfa(self,formula:Formula)->DFA:"""From LDL to DFA."""defpltl2dfa(self,formula:Formula)->DFA:"""From PLTL to DFA."""defpldl2dfa(self,formula:Formula)->DFA:"""From PLDL to DFA."""deffol2dfa(self,formula:Formula)->DFA:"""From FOL to DFA."""defmso2dfa(self,formula:Formula)->DFA:"""From MSO to DFA."""Then, you can register the custom backend
class in the library:fromlogaut.backendsimportregisterregister(id_="my_backend",entry_point="dotted.path.to.MyBackend")And then, use it through the main entry point:dfa=ltl2dfa(formula,backend="my_backend")TestsTo run tests:toxTo run only the code tests:tox -e py3.7To run only the linters:tox -e flake8tox -e mypytox -e black-checktox -e isort-checkPlease look at thetox.inifile for the full list of supported commands.DocsTo build the docs:mkdocs buildTo view documentation in a browser:mkdocs serveand then go tohttp://localhost:8000Licenselogaut is released under the GNU Lesser General Public License v3.0 or later (LGPLv3+).Copyright 2021 WhiteMechAuthorsMarco Favorito
Package: logavu
LogaVuLog Analyzer and VisualizerUsageHere is an example usingloguruPut this code in the Python program you want to use with LogaVuimportsocketfromloguruimportloggerHOST,PORT='localhost',5005sock=socket.socket(socket.AF_INET,# Internetsocket.SOCK_DGRAM)# UDPdeflogudp(message:str):sock.sendto(bytes(message,'utf-8'),(HOST,PORT))logger.add(logudp)
Package: logbesselk
logbesselkProvide function to calculate the modified Bessel function of the second kind
and its derivatives.ReferenceTakashi Takekawa, Fast parallel calculation of modified Bessel function
of the second kind and its derivatives, SoftwareX, 17, 100923, 2022.AuthorTAKEKAWA Takashitakekawa@tk2lab.orgFor TensorflowRequirePython (>=3.8)Tensorflow (>=2.6)InstallationpipinstalltensorflowlogbesselkExamplesimporttensorflowastffromlogbesselk.tensorflowimportlog_bessel_kaslogkfromlogbesselk.jaximportbessel_keaskefromlogbesselk.jaximportbessel_kratioaskratiov=1.0x=1.0a=logk(v,x)v=jnp.linspace(1,10,10)x=jnp.linspace(1,10,10)b=logk(v,x)# gradientwithtf.GradientTape()asg:g.watch(v,x)f=logk(v,x)dlogkdv=g.gradient(f,v)dlogkdx=g.gradient(f,x)# use tf.functionlogk=tf.function(logk)# advanced versionfromlogbesselk.tensorflowimportlog_abs_deriv_bessel_klogk=lambdav,x:log_abs_deriv_bessel_k(v,x,0,0)logdkdv=lambdav,x:log_abs_deriv_bessel_k(v,x,1,0)logdkdx=lambdav,x:log_abs_deriv_bessel_k(v,x,0,1)For jaxRequirePython (>=3.8)jax (>=0.3)Installationpipinstalljax[cuda]-fhttps://storage.googleapis.com/jax-releases/jax_cuda_releases.html
pipinstalllogbesselkExamplesimportjaximportjax.numpyasjnpfromlogbesselk.jaximportlog_bessel_kaslogkfromlogbesselk.jaximportbessel_keaskefromlogbesselk.jaximportbessel_kratioaskratio# scalar func and gradv=1.0x=1.0a=logk(v,x)# dlogK/dv = (dK/dv) / Kdlogkdv=jax.grad(logk,0)b=dlogkdv(v,x)# dlogK/dx = (dK/dx) / Kdlogkdx=jax.grad(logk,1)c=dlogkdx(v,x)# miscd=ke(v,x)e=kratio(v,x,d=1)# vectorizelogk_vec=jax.vmap(logk)v=jnp.linspace(1,10,10)x=jnp.linspace(1,10,10)f=logk_vec(v)# use jitlogk_vec_jit=jax.jit(logk_vec)# advanced versionfromlogbesselk.jaximportlog_abs_devel_bessel_klog_dkdv=lambdav,x:log_abs_deriv_bessel_k(v,x,1,0)log_dkdx=lambdav,x:log_abs_deriv_bessel_k(v,x,0,1)log_dkdv_jit=jax.jit(jax.vmap(log_dkdv))log_dkdx_jit=jax.jit(jax.vmap(log_dkdx))
Package: logbind
logbindEasily bind new fields into your logger instances.# Original loggerlogger=logging.getLogger('a')logger.info('Hello')logger=logbind.bind(logger,id=12345)logger.info('Hello')# <- This logrecord has an extra field "id"logger=logbind.bind(logger,abc=67890)logger.info('Hello')# <- logrecord has two extra fields: "id" and "abc"This feature will be most useful with a structured logging formatter,
such as JSON.  You could trylogjsonfor example.
Package: logBlizzard
The goal is to provide distributed logging aggregation points, initially for syslog with longer term plans for other on-box logs. Aggregation points can be implemented close to the logging source either on a network device or other 3rd party compute node. Logging can be segmented into clusters with a hierarchy in each cluster. Message back-up/availability is built into the cluster logging architecture. Message dedup is the function of the ‘search head’. Each cluster has its own northbound API front end as part of a search head to be integrated with other systems.
Package: logboard
logboardMonitor and Compare Logs on Browser/Terminal.DescriptionInspired bytensorboard,gripandnotable,
all of which serve light-weight GUI byonly using static files (e.g., markdown files without DB);single command (e.g.,tensorboard --logdir logs/andgrip README.md).Why nottensorboard?I also usetensorboardin addition to this tool.
But currentlytensorboarddoesn't support comparing different configurations
for each log (e.g., git-hash of the code, learning rate, training strategy).logboardis a kind of extra plugin totensorboard(but you need to run in different terminal, unfortunately).
I expect this kind of feature will be included intensorboardin the future.InstallationpipinstalllogboardUsageBrowser (logboard --logdir logs/)$cdexamples

$catlogs/20190310_093252.724597/args{"loglevel":"info","gpu":0,"seed":0,"class_ids":[1],"lr":0.001,"timestamp":"2019-03-10T09:32:52.724597","out":"/home/wkentaro/logboard/examples/logs/20190310_093252.724597","hostname":"computer1","githash":"b48ce48"}# similar to tensorboard --logdir logs/$logboard--logdirlogs/--filterouttimestamploglevelgpuseed'lr .*''.*main/loss.*(max)''.*loss_.*'Terminal (logtable --logdir logs/)$cdexamples

$logtable--logdirlogs--filterouttimestamploglevelgpuseed'lr .*''.*main/loss.*(max)''.*loss_.*'*Logdirectory:logs
╒════╤════════════════════════╤═════════╤═════════════╤════════════════╤═══════════════════╤═════════════╤═══════════╤════════════╤═══════╤══════════════╤═══════════════╕
││log_dir│epoch│iteration│elapsed_time│updated_at│class_ids│githash│hostname│lr│main/│validation/│
│││││││││││loss(min)│main/│
││││││││││││loss(min)│
╞════╪════════════════════════╪═════════╪═════════════╪════════════════╪═══════════════════╪═════════════╪═══════════╪════════════╪═══════╪══════════════╪═══════════════╡
│0│20190310_093252.724597│1│1740│1:47:02│88days,14:24:22│[1]│b48ce48│computer1│0.001│0.0088│0.18│
│││││││││││(1,1580)│(0,880)│
├────┼────────────────────────┼─────────┼─────────────┼────────────────┼───────────────────┼─────────────┼───────────┼────────────┼───────┼──────────────┼───────────────┤
│1│20190310_093829.691289│1│1720│1:45:37│88days,14:24:22│[1]│f766b97│computer2│0.001│0.012│0.19│
│││││││││││(1,1620)│(0,440)│
╘════╧════════════════════════╧═════════╧═════════════╧════════════════╧═══════════════════╧═════════════╧═══════════╧════════════╧═══════╧══════════════╧═══════════════╛
Package: logbook-aiopipe
logbook_aiopipe -- Multiprocess logbook logging for asyncioDocumentationThis package provides a handler and subscriber for multiprocesslogbooklogging that runs on theasyncioevent loop. It usesaiopipeto transfer log messages from the child
process to the parent process.ExampleThe following example shows a typical application of multiprocess logging. It results in
two log messages,hello from parent processandhello from child process, being
printed in some order.fromcontextlibimportclosingfrommultiprocessingimportProcessimportasynciofromaiopipeimportaiopipefromlogbook_aiopipeimportAioPipeSubscriber,\AioPipeHandlerfromlogbookimportLogger,StderrHandlerasyncdefmainTask(eventLoop):# The parent process logger can be set up as normal.log=Logger()log.handlers.append(StderrHandler())rx,tx=aiopipe()sub=AioPipeSubscriber(awaitrx.open(eventLoop),log)withclosing(sub):subTask=eventLoop.create_task(sub.run())withtx.send()astx:proc=Process(target=childProc,args=(tx,))proc.start()log.info("hello from parent process")proc.join()awaitsubTaskdefchildProc(tx):eventLoop=asyncio.new_event_loop()eventLoop.run_until_complete(childTask(eventLoop,tx))asyncdefchildTask(eventLoop,tx):log=Logger()# The child process should use only `AioPipeHandler` as# its handler.handler=AioPipeHandler(awaittx.open(eventLoop))log.handlers.append(handler)withclosing(handler):log.info("hello from child process")eventLoop=asyncio.get_event_loop()eventLoop.run_until_complete(mainTask(eventLoop))InstallationThis package requires Python >= 3.5.0 and can be installed withpip:pip install logbook_aiopipe
Package: logbook-cli
logbook-cliKeep Your Daily Events Recorded on Your Logbook Using Command Line.How Does It Work?logbook-cliis created using python. It usestyperfor the Command Line Interface
andSQLAlchemyto interact with the database (sqlite).
All the log entries are stored into thesqlitedatabase.InstallationYou can installlogbook-cliby usingpip:pip install logbook-cliConfigurationlogbook-clistores thesqlitedatabase in~/.logbook/directory by default.You can useLOG_BOOK_DATABASE_URLenvironment variable to use a different location.Example:export LOG_BOOK_DATABASE_URL=sqlite:///logbook.sqlite3How to uselogbook-cliUsage$logbook-cli[OPTIONS]COMMAND[ARGS]...Options:--install-completion: Install completion for the current shell.--show-completion: Show completion for the current shell, to copy it or customize the installation.--help: Show this message and exit.Commands:add: Add a log entry to the logbook.delete: Delete a log entry using it's ID.edit: Update a log entry using it's ID.find: List all log entries that match the argument.list: List all log entries in a table, limits upto...view: View a single log entry using it's ID.logbook-cli addAdd a log entry to the logbook.Usage:$logbook-cliadd[OPTIONS]DESCRIPTIONArguments:DESCRIPTION: Description of the log entry  [required]Options:-d, --date [%Y-%m-%d|%Y-%m-%dT%H:%M:%S|%Y-%m-%d %H:%M:%S]: Date of the log entry  [default:log entry date]-t, --time [%H:%M:%S|%I:%M %p]: Time of the log entry  [default:log entry time]--help: Show this message and exit.Example:$logbook-cliadd-d"2021-05-10"-t"10:00 PM""This is a Test Log Entry"logbook-cli deleteDelete a log entry using it's ID.Usage:$logbook-clidelete[OPTIONS]IDArguments:ID: ID of the log entry  [required]Options:--help: Show this message and exit.Example:$logbook-clidelete1logbook-cli editUpdate a log entry using it's ID.Usage:$logbook-cliedit[OPTIONS]IDArguments:ID: ID of the log entry  [required]Options:--description TEXT: New Description for the log entry  [default: ]-d, --date [%Y-%m-%d|%Y-%m-%dT%H:%M:%S|%Y-%m-%d %H:%M:%S]: New Date for the log entry-t, --time [%H:%M:%S|%I:%M %p]: New Time for the log entry--help: Show this message and exit.Example:$logbook-cliedit1-d"2021-05-10"-t"10:00 PM"--description"This is a Edited Test Log Entry"logbook-cli findList all log entries that match the argument.Usage:$logbook-clifind[OPTIONS]DESCRIPTION_CONTAINSArguments:DESCRIPTION_CONTAINS: String that may match log entry description  [required]Options:--help: Show this message and exit.Example:$logbook-clifind"test"logbook-cli listList all log entries in a table, limits upto 40 log entries.Usage:$logbook-clilist[OPTIONS]Options:--help: Show this message and exit.Example:$logbook-clilistlogbook-cli viewView a single log entry using it's ID.Usage:$logbook-cliview[OPTIONS]IDArguments:ID: ID of the log entry  [required]Options:--help: Show this message and exit.Example:$logbook-cliview1Screenshot
Package: logbook-logstash
JSON logs with logstash format for logbookThis library is provided to allowlogbooklogging to output log
data as json objects ready to be shipped out to logstash.This project is a fork ofexoscale/python-logstash-formatterInstallingPip:$ pip install logbook-logstashPypi:https://pypi.python.org/pypi/logbook-logstashManual:$ python setup.py installUsageJson outputs are provided by the LogstashFormatter logging formatter,
for instance:importsysimportlogbook_logstashfromlogbookimportLogger,StreamHandlerlog=Logger('testlog')handler=StreamHandler(sys.stdout)handler.formatter=logbook_logstash.LogstashFormatter()handler.push_application()log.info('My test')You can provide extra variables, and show the exception traceback.log.info({"account":123,"ip":"172.20.19.18"})log.info("classic message for account:%s",account,extra={"account":account})try:h={}h['key']except:log.info("something unexpected happened",exc_info=True)Sample outputThe following keys will be found in the output JSON:@source_host: source hostname for the log@timestamp: ISO 8601 timestamp@message: short message for this log@fields: all extra fields{"@fields":{"account":"pyr","args":[],"created":1367480388.013037,"exception":["Traceback (most recent call last):\n","  File\"toto.py\", line 16, in <module>\nk['unknown']\n","KeyError: 'unknown'\n"],"filename":"toto.py","funcName":"<module>","levelname":"WARNING","levelno":30,"lineno":18,"module":"toto","msecs":13.036966323852539,"name":"root","pathname":"toto.py","process":1819,"processName":"MainProcess","relativeCreated":18.002986907958984,"thread":140060726359808,"threadName":"MainThread"},"@message":"TOTO","@source_host":"phoenix.spootnik.org","@timestamp":"2013-05-02T09:39:48.013158"}TestsThis project has basic tests, and uses thepytestlibrary. Just
execute the following commands in the project root.$ pip install -r dev-requirements.txt
$ py.test
Package: logbot
A logging bot for XMPP chat rooms.# What?
* XMPP Log bot
* Search interface (with whoosh and flask)
* Log to text files# Installpip install logbot# Runninglogbot–host chat.looney.org –userlogbot@chat.looney.org–passwd  S3cr3tyada@conference.looney.orgWeb interface at [http://localhost:5000](http://localhost:5000)# BugsVersion 0.3.0 changed search schema. Please runutils/upgrade_03.pyto update
the schema.# Contact
Miki Tebeka <miki.tebeka@gmail.com>Bugs go [here](https://bitbucket.org/tebeka/logbot/issues)<!—
vim: spell
–>
Package: logbt
Configuration for LoguruInstallpoetryaddlogbtUsageimportloggingfromlogbtimportconfigfromlogbtimportinject_loguru_to_loggingfromlogbtimportloggerconfig(level="INFO")deff():logger.info("INSIDE function")logger.debug("YOU SHOULD NOT SEE THIS")logger.info("THIS IS INFO")logger.error("THIS IS NOT INFO")f()inject_loguru_to_logging("test")logging_logger=logging.getLogger("test")logging_logger.setLevel(logging.INFO)logging_logger.info("Inject logging%s","test")