Saravanan666/pypi-org huggingface dataset:
Package: servicex-dashboard
servicex-dashboardCreate a JupyterLab plugin that displays data from the ServiceX DashboardRequirementsJupyterLab >= 3.0InstallTo install the extension, execute:pipinstallservicex-dashboardUninstallTo remove the extension, execute:pipuninstallservicex-dashboardContributingDevelopment installNote: You will need NodeJS to build the extension package.Thejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Change directory to the servicex-dashboard directory# Install package in development modepipinstall-e.# Link your development version of the extension with JupyterLabjupyterlabextensiondevelop.--overwrite# Rebuild extension Typescript source after making changesjlpmbuildYou can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.# Watch the source directory in one terminal, automatically rebuilding when neededjlpmwatch# Run JupyterLab in another terminaljupyterlabWith the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).By default, thejlpm buildcommand generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:jupyterlabbuild--minimize=FalseDevelopment uninstallpipuninstallservicex-dashboardIn development mode, you will also need to remove the symlink created byjupyter labextension developcommand. To find its location, you can runjupyter labextension listto figure out where thelabextensionsfolder is located. Then you can remove the symlink namedservicex-dashboardwithin that folder.Testing the extensionFrontend testsThis extension is usingJestfor JavaScript code testing.To execute them, execute:jlpm
jlpmtestIntegration testsThis extension usesPlaywrightfor the integration tests (aka user level tests).
More precisely, the JupyterLab helperGalatais used to handle testing the extension in JupyterLab.More information are provided within theui-testsREADME.Packaging the extensionSeeRELEASE
Package: servicex-transformer
ServiceX_transformer LibraryLibrary of common classes for building serviceX transformers.Minimum RequiremntsWorks with Python version 2.7 and aboveDownload from PyPiTo use this library:pipinstallservicex-transformerStandard Command Line ArgumentsThis library provides a subclass of ArgParse for standardizing commnand line
arguments for all transformer implementations.Available arguments are:Transformed Result OutputCommand line arguments determine a destination for the results as well as
an output format.Object Store - Each transformed file is written as an object to an
S3 compatible object store. The only currently supported output file
format is parquet. The objects are stored in a bucket named after the
transformation request ID.Command Line ReferenceOptionDescriptionDefault--tree TREERoot Tree to extract data from. Only valid for uproot transformerEvents--path PATHPath to single Root file to transform. Any file path readable by xrootd--limit LIMITMax number of events to process--result-destination DESTWhere to send the results: object-store, output-dir, volumeobject-store--output-dirLocal directory where the result will be written. Use this to run standalone without other serviceX infrastructureNone--result-formatBinary format for the results: arrow, parquet, or root-filearrow--rabbit-uri URIRabbitMQ Connection URIhost.docker.internal--request-id GUIDID associated with this transformation request. Used as RabbitMQ Topic Name as well as object-store bucketservicexRunning TestsValidation of the code logic is performed usingpytestandpytest-mock. Unit test fixtures are
intestdirectories inside each package.The tests are instrumented with code coverage reporting viacodecov. The travis
job has a the codecov upload token set as an environment variable which is
passed into the docker container so the report can be uploaded upon successful
conclusion of the tests.Coding StandardsTo make it easier for multiple people to work on the codebase, we enforce PEP8
standards, verified by flake8. The community has found that the 80 character
limit is a bit awkward, so we have a local config setting themax_line_lengthto 99.
Package: serving-utils
serving-utilsSome python utilities when using tensorflow-serving.InstallationPrepare an environment with python version >= 3.6From PYPI: 1. Manually install tensorflow CPU or GPU version. 2.pip installserving-utilsFrom Github repository: 1.git clonegit@github.com:Yoctol/serving-utils.git2. Manually
install tensorflow CPU or GPU version. 3.make installUsageSaver and Loader ```python import tensorflow as tffrom serving_utils.saver import Saver from serving_utils.loader import
Loadersaver = Saver( session=tf.Session(graph=your_graph),
output_dir=’/path/to/serving’, signature_def_map={ ‘predict’:
tf.saved_model.signature_def_utils.predict_signature_def(
inputs={‘input’: tf.Tensor…}, outputs={‘output’: tf.Tensor…}, ) },
freeze=True, # (default: True) Frozen graph will be saved if True. )
saver.save()loader = Loader( path=’/path/to/serving’, # version=1, # if not
specified, use the latest version ) new_sess = tf.Session()loader.load(new_sess) # load the saved model into new session ```Client ```python from serving_utils import Client client =
Client(host=”localhost”, port=8500, n_trys=3) client.predict(
{‘input’: np.ones(1, 10)}, output_names=[‘output’],
model_signature_name=’predict’, )or asyncawait client.async_predict(…) ```Freeze graph ```python from serving_utils.freeze_graph import
freeze_graph, create_session_from_graphdeffrozen_graph_def = freeze_graph(session, output_op_names)
new_session = create_session_from_graphdef(frozen_graph_def)## Test

Run the following commands:make lint make test## Devmake install-dev### Protospython -m grpc_tools.protoc -I. –python_out=. –python_grpc_out=.
–grpc_python_out=. serving_utils/protos/*.proto ```
Package: servir
serviran extensible async background server for pythontable of contentsinstallationusagelicenseinstallationpip install servirusageimportpathlibimportrequestsfromservirimportProvider# create a providerprovider=Provider()### File (supports range requests)path=pathlib.Path("hello.txt")path.write_text("hello, world")file_resource=provider.create(path)response=requests.get(file_resource.url)assertresponse.text=="hello, world"assert"text/plain"inresponse.headers["Content-Type"]### Directory (supports range requests)root=pathlib.Path("data_dir")root.mkdir()(root/"hello.txt").write_text("hello, world")dir_resource=provider.create(root)response=requests.get(file_resource.url+"/hello.txt")assertresponse.text=="hello, world"assert"text/plain"inresponse.headers["Content-Type"]### In-Memorydata="a,b,c,\n1,2,3,\n4,5,6"content_resource=provider.create(data,extension=".csv")response=requests.get(content_resource.url)assertresponse.text==dataassert"text/csv"inresponse.headers["Content-Type"]Note: theProviderholds aweak referenceto each resource it creates.
This allows the provider to cleanup unused resources and prevent memory leaks.
As an end user, you must hold astrongreference each resource returned by
the provider so long as you'd like that endpoint to remain avaiable.licenseserviris distributed under the terms of theMITlicense.
Package: servir-aces
ACES (Agricultural Classification and Estimation Service)ACES (Agricultural Classification and Estimation Service) is a Python module for generating training data and training machine learning models for remote sensing applications. It provides functionalities for data processing, data loading from Earth Engine, feature extraction, and model training.FeaturesData loading and processing from Earth Engine.Generation of training data for machine learning models.Training and evaluation of machine learning models (DNN, CNN, UNET).Support for remote sensing feature extraction.Integration with Apache Beam for data processing.UsageDefine all your configuration in.envfile. An example of the file is provided as.env.examplefile.Here's an example of how to use the ACES module:fromaces.configimportConfigfromaces.model_trainerimportModelTrainerif__name__=="__main__":config=Config()trainer=ModelTrainer(config)trainer.train_model()ContributingContributions to ACES are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on the GitHub repository.LicenseThis project is licensed under theGNU General Public License v3.0.
Package: servirtium
Main Servirtium site:http://servirtium.devA Servirtium library for PythonDemo project that uses it:https://github.com/servirtium/demo-python-climate-data-tckAlpha quality softwareWorking so far:Record and playback of GET/POST/PUTNot Working yet:Support for "Transfer-Encoding" header for "chunked" responseHelp needed from Pythonistas!Building Servirtium lib/package for Python3Execute the following commands to install the package locally:pip3 install requests
cd servirtium-python
pip3 install -e .OR install it fromhttps://pypi.org/using following:pip3 install servirtiumThe 'demo-python-climate-data-tck' demo needs you to have built the Python version of Servirtium
first as it is presently unpublished in pip-land.Running unit testspip3 install pytest
cd servirtium-python
pytestRunning the compatibility suiteThis should record a bunch of interactions, using a Mocha test suite that we launch via Selenium-WebDriverpython3 -m test.compatibility-suite record
Package: servoarray
servoarrayPluggable servomotor driver libraryInstallBoost.Filesystemis requiredpipinstallservoarrayUsagefromservoarrayimportServoArraysa=ServoArray()# You can specify driver name and parameters directly though it is not recommended# sa = ServoArray("pca9685", {"bus": 1, "address": 0x40, "min_pulse": 150, "max_pulse": 600})# Now you can set a radian value to the servosa[0]=3.14/2# Maxsa[0]=-3.14/2# Min# Slicing and reverse indexing are supported as the normal listsa[:]=[0]*len(sa)print(sa[-1])
Package: servodoor
Servodoor: a simple library for controlling RC servo based doorsServo door is a simple library for controlling (RC servo) doors  via via
Pimoroni'sServo 204018-channel servo controller. Initially designed for use in T-maze type
experiments. The Serovo 2040 must be running theservodoor-firmwareInstallingInstall using pip$pipinstallservodoorInstalling from sourceThis package uses thepoetrydependency manager.
The installation instructions for poetry can be foundhereOnce poetry is installed the serovodoor package can be installed using$poetryinstallAdditional documentation on using poetry can be foundhereUsageimporttimefromservodoorimportServoDoorport='/dev/ttyACM0'ctrl=ServoDoor(port)rsp=ctrl.get_config()print(rsp['config'])rsp=ctrl.get_positions()print(rsp['positions'])ctrl.set_doors({'front':'open'})time.sleep(5.0)ctrl.set_doors({'front':'close'})time.sleep(5.0)
Package: servodoor-app
Servodoor-app: RC servo based door control applicationA simple GUI for controlling up to 18 (RC servo) doors. Designed for use
with an automated T-Maze system.InstallingInstall using pip$pipinstallservodoor-appInstalling from sourceThis package uses thepoetrydependency manager.
The installation instructions for poetry can be foundhereOnce poetry is installed the serovodoor-app package can be installed using$poetryinstallAdditional documentation on using poetry can be foundhereGUI imagesExample door configuration file.{"front":{"servo":1,"open":1375,"close":1987,"max_vel":40000.0,"max_acc":40000.0},"left":{"servo":2,"open":1340,"close":1947,"max_vel":40000.0,"max_acc":40000.0},"right":{"servo":3,"open":1300,"close":1890,"max_vel":40000.0,"max_acc":40000.0},"sleep_front":{"servo":4,"open":1300,"close":1890,"max_vel":7000.0,"max_acc":2000.0},"sleep_back":{"servo":5,"open":1300,"close":1890,"max_vel":7000.0,"max_acc":2000.0}}
Package: servo-lock
ContextThis tool is showcased in the projectIotaWorkshopSummaryThis tool can be both used as a command line interface or be imported into your project. What this repo has that others do not is some math to calculate the estimated time it takes to rotate the servo to a sepecific destination. The utility handles all the pin IO and all you have to do is specify the location the servo needs to be in and the library will do the rest.InstallationThis project requiresRPi.GPIO, and I installed it using the arch repo using,yay -S python-raspberry-gpioBut ubuntu or rasbian can use,pip install RPi.GPIOTo install the servo library simply pull from the pip repo,pip install servo-lockCommand Line Interface ExamplesTo send the servo to a sepecific location simply use the commandservo move 19 90 #Moves the servo on pin 19 to 90 degrees
Package: servo_tidy
Servo’s code and license checker.InstallationInstall from PyPI:pip install servo-tidyor from git:pip install -e git+https://github.com/servo/servo.git#egg=servo_tidy&subdirectory=python/tidyTo run the tests./mach test-tidy --self-testRelease History0.3.0 (2017-02-24)Added checking for blocked packagesAdded checking for alphabetical order for JSON keys and#![feature(...)]Improved duplicated packages checkingImproved orderingusestatementsAllow tidy to run custom project-specific lintsAnd many other improvements0.2.0 (2016-08-09)Improve license checking to disregard comments and line breaksLicense checking verifies that COPYRIGHT is specified when apache2 is used0.0.3 (2016-04-19)Add alternate wording of apache2 license0.0.2 (2016-04-17)Cleanup Tidy to work on external deps0.0.1 (2016-04-12)Package Tidy
Package: servpy
ServPyServPy is a Python library that allows you to bruteforce and find (ssh, ftp, windows) server in the internet by checking open portsInstallationUse the package managerpipto install servpy.pipinstallservpyUsageimportthreadingfromservpyimportServerdefmain():rdp=Server()# print(f'Running.... {threading.current_thread()}')rdp.run(verbose=False)defthreads(thread:int):thread_list=[]for_inrange(thread):t=threading.Thread(target=main)thread_list.append(t)t.start()forthread_inthread_list:thread_.join()if__name__=='__main__':threads(100)ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: servus
ServusA wrapper for the aiohttp library for making asynchronous web requests in Python.Trying to preserve speed and flexibility provided byaiohttp, without sacrificing the human-friendliness ofrequests,servusabstracts using client sessions and context managers when making asynchronous HTTP requests in Python.Example usage:importservusimportaiohttpimportasyncioasyncdefmain():# Create a new sessionmySession=aiohttp.ClientSession()# Use Servus to send a request.# Servus automatically parses and serializes the response, and returns a ready to use objectresponse=awaitservus.get(mySession,"http://httpbin.org")print(response.response)# (aiohttp.ClientResponse )print(response.json)# (dict)# Remeber to close the session!mySession.close()asyncio.run(main())servusalso has inbuilt support for working with Discord bots.Example Usage:importdiscordfromdiscord.extimportcommandsimportasyncioimportservusfromservus.discord_utilsimportcreateRequestsClientMY_TOKEN="<YOUR_TOKEN>"bot=commands.Bot(command_prefix=commands.when_mentioned_or("!"))@bot.command()asyncdefhello(ctx):"""Hello world, with a HTTP request!"""r=awaitservus.get(bot.session,"https://httpbin.org")data=r.jsonawaitctx.send(f"World!{data}")# Add the createRequestClient coroutine to `bot` async loopbot.loop.create_task(createRequestsClient(bot))# Run the botbot.run(MY_TOKEN)
Package: servusdates
servusdatesPython3 module to check date and hour format.InstallationInstall with pippip3 install -U servusdatesUsageIn [1]: import servusdates

In [2]: servusdates.is_complete_hour("16:20:00")

Out[2]: True

In [3]: servusdates.is_date("08/10/1997")

Out[3]: (True, 'DD-MM-YYYY')
Package: servusnumre
servusnumrePython3 module with regex patterns related to numbers.InstallationInstall with pippip3 install -U servusnumreUsageIn [1]: import servusnumre

In [2]: servusnumre.float_pattern

Out[2]: '[-+]?[0-9]*[\\.,]?[0-9]+'

In [3]: servusnumre.exponent_pattern

Out[3]: '[eE]?[-+]?[0-9]?'
Package: servussimplifytext
servussimplifytextPython3 module to simplify text removing unnecesary symbols.InstallationInstall with pippip3 install -U servussimplifytextUsageIn [1]: import servussimplifytext

In [2]: servussimplifytext.simplify_text("Hola. á")
Out[2]: 'Hola.'

In [3]: servussimplifytext.simplify_text_no_symbols("Hola. á")
Out[3]: 'Hola'
Package: servussymtowords
servussymtowordsPython3 module to convert temperature units and Km to words.InstallationInstall with pippip3 install -U servussymtowordsUsageIn [1]: import servussymtowords

In [2]: servussymtowords.units_and_numbers_to_words(
    "Actual temperature is of 5°C, with a wind of 1Km/h"
    )
Out[2]: 'Actual temperature is of five Celsius degrees, with a wind of one Kilometer per hour'
Package: servustextnormaliser
servustextnormaliserPython3 module to normalise text.InstallationInstall with pippip3 install -U servustextnormaliserUsageIn [1]: import servustextnormaliser

In [2]: text_normaliser = servustextnormaliser.TextNormaliser()

In [3]: text_normaliser.normalise("Hello, ¡I'll be normalised!")

Out[3]: 'Hello I will be normalised'
Package: servyou-log
Representation of log tool.The proper way to get an instance of this class is to call
logging.Logger.

Provide a tool for write runtime log to the file. Accepts several
arguments:

:param name: log file name
:param is_add_stream_handler: Whether to print the log to the console
:param do_not_use_color_handler: Whether forbid log color.
:param log_path: file log path.
:param log_file_size: Limit log file size.
:param log_file_handler_type: Select log header type .
:param formatter_template: Fixed log template.
Package: servz
ServzMachine Learning Model ServingThis library is a pre-alpha right nowThe intent of this package is to provide a machine learning deplpoyment layer for model predictions.Featurespipeline drivendeployment via task runnerdeployment via flask or other endpointMLFlow based deploymentSeldon deployment (in progress)Model Serving ArchitecturesModel as CodeModel is written by some developer and relatively standard IT DevOps procedures used to bring model into production.Frameworks using the MoC architecture include:MLFlowSeldonClipperTensorflow Servingpros:easy developmentdata scientist does not need to be an SRE or deal with deploymentautomation (of standardized parts, if any)model state included in production codecons:ever increasing complexities with scaleincreased latency, becomes a bottleneck at scaledifferent toolsets useddifficult to updatehard to rebuildoverall lack of scaleModel as DataThe model is implemented via a parameter file of some kind.Data formats used in MaD architectures include:Tensorflow SavedModelksPMMLPFAONNXFrameworks using MaD concepts include:LightbendAkka ServingSpark Structured StreamingFlinkKafka Queryable Statepros:simple model managementmodel standardizationlow latencyeasy to implementforces cross-silo communicationcons:not all tools support model formatsstandardization still in early stagesOther Model Serving PatternsTBACurrent opinionFor workloads at low scale use MaCFor workloads at high scale (aka batch() use MaDstructure/base  - common library
/core - core files for servz
/orchestration_artifact_builder - artifact packager
/orchestration_artifact_deployer - deployment runner
/packager - manifest packager
/pipeline - loading and validation of serving pipeline
/server_templates - artifacts for artifact builder to construct endpoints
/tests - unit tests and e2e tests

```
Package: sesame
Almost all applications have configuration of some kind, and often this config
is sensitive - database passwords, SMTP account details, API keys etc.These days it’s common to use public source control; which means you can no
longer store your application’s sensitive config with your code.Sesame provides a simple way to encrypt (and decrypt) your application’s config
so it can be safely stored in public source control.CryptographySesame leans on a little known project calledkeyczar,
which was originally built by members of the Google Security Team.Keyczar in turn builds uponpycryptowhich aims to provide sane defaults for your Python crypto.InstallationTo installsesame, simply:$pipinstall--presesameSince pip version 1.4 the--preparameter is necessary sincekeyczaris
still an alpha release. People using earlier versions of pip can omit the--pre.UsageThe interface to Sesame intentionally resembles that oftar. There are only two
sub-commands:encryptanddecryptas described below:usage:sesameencrypt[-h][-kKEYFILE][-f]outputfileinputfile[inputfile...]positionalarguments:outputfileEncryptedfiletobecreatedinputfileFilestobeencryptedoptionalarguments:-h,--helpshowthishelpmessageandexit-kKEYFILE,--keyfileKEYFILEPathtokeyczarencryptionkey-f,--forceForceoverwriteofexistingencryptedfileusage:sesamedecrypt[-h][-kKEYFILE][-f][-OOUTPUT_DIR][-T]inputfilepositionalarguments:inputfileFiletobedecryptedoptionalarguments:-h,--helpshowthishelpmessageandexit-kKEYFILE,--keyfileKEYFILEPathtokeyczarencryptionkey-f,--forceForceoverwriteofexistingdecryptedfile-OOUTPUT_DIR,--output-dirOUTPUT_DIRExtractfilesintoaspecificdirectory-T,--try-allSearchforkeysfromcurrentdirectoryandtryallofthem
Package: sesameeg
SESAMEEG: SEquential Semi-Analytic Montecarlo Estimation for MEEGThis is a Python3 implementation of the Bayesian multi-dipole modeling method and Sequential Monte Carlo algorithm
SESAME described in[1].
The algorithm takes in input a forward solution and a MEEG evoked data time series, and outputs a posterior
probability map for brain activity, as well as estimates of the  number of sources, their locations and their
amplitudes.InstallationTo install this package, the easiest way is usingpip. It will install this
package and its dependencies. Thesetup.pydepends onnumpy,scipyandmnefor the installation so it is advised to install them beforehand. To
install this package, please run the following commands:(Latest stable version)pip install numpy scipy mne
pip install sesameegIf you do not have admin privileges on the computer, use the--userflag
withpip. To upgrade, use the--upgradeflag provided bypip.To check if everything worked fine, you can run:python -c 'import sesameeg'and it should not give any error messages.Bug reportsUse thegithub issue trackerto report bugs.Authors of the codeGianvittorio Luria <luria@dima.unige.it>,Sara Sommariva <sommariva@dima.unige.it>,Alberto Sorrentino <sorrentino@dima.unige.it>.Cite our workIf you use this code in your project, please consider citing our work:[1]Sommariva and A. Sorrentino,Sequential Monte Carlo samplers for semi-linear inverse problems and application to Magnetoencephalography. Inverse Problems, 30 114020 (2014).