Saravanan666/pypi-org huggingface dataset:
Package: loopmon
loopmon - Lightweight Event Loop monitoringloopmon is a lightweight library that can detect throttling of event loops.
For example, you can detect long-running coroutine or whether the blocking function is invoked inside the event loop.Usageimportasyncioimporttimefromdatetimeimportdatetimeimportloopmonasyncdefprint_collected_data(lag:float,tasks:int,data_at:datetime)->None:print(f'event loop lag:{lag:.3f}, running tasks:{tasks}, at{data_at}')asyncdefmain()->None:loopmon.create(interval=0.5,callbacks=[print_collected_data])# Simple I/O bound coroutine does not occur event loop lagawaitasyncio.sleep(0.2)# Blocking function calltime.sleep(1)if__name__=='__main__':asyncio.run(main())will prints:event loop lag: 0.000, running tasks: 2, at 2022-02-24 13:29:05.367330+00:00
event loop lag: 1.001, running tasks: 1, at 2022-02-24 13:29:06.468622+00:00You can check otherexamples.I recommend you to addloopmon.create(...)on beginning of async function if you are not familiar with handling loop itself.
But you can also control creation, installation or staring of monitor viaEventLoopMonitor.start()orEventLoopMonitor.install_to_loop().FeaturesDetects event loop lagDetects event loop running on other thread.exampleCollect how many tasks are running in the event loopCustomize monitoring start and end pointsCustomize monitoring intervalCustomize collected metrics through callbacks100% type annotatedZero dependency (excepttyping-extentions)How it worksEvent loop is single threaded and based on Cooperative Scheduling.
So if there is a task that does not yield to another tasks, any tasks on the loop can not be executed.
And starvation also happens when there are too many tasks that a event loop can not handle.Currentlyloopmon.SleepEventLoopMonitoris one and only monitor implementation.
It periodically sleeps with remembering time just before sleep, and compares the time after awake.
The starvation happen if the difference bigger than its sleeping interval.pseudo code ofSleepEventLoopMonitorwhileTrue:before=loop.time()awaitasyncio.sleep(interval)lag=loop.time()-before-intervaltasks=len(asyncio.all_tasks(loop))data_at=datetime.now(timezone.utc)forcincallbacks:loop.create_task(c(lag,tasks,data_at))Integration examplesPrometheusfromdatetimeimportdatetimefromfunctoolsimportpartialimportloopmonfromprometheus_clientimportGaugeasyncdefcollect_lag(gauge:Gauge,lag:float,_:int,__:datetime)->None:gauge.set(lag)asyncdefmain(gauge:Gauge)->None:loopmon.create(interval=0.5,callbacks=[partial(collect_lag,gauge)])...
Package: loop-python
Welcome toloopReplace common looping boilerplate with pretty method chaining.Installationpipinstallloop-pythonDocumentationVisit thedocumentation site.ExampleLoop overrange(20), apply thefibo()function and then, on top of it, apply thefizzbuzz()function, finally, applyprint():fromloopimportloop_overdeffizzbuzz(x):rules={3:'fizz',5:'buzz'}.items()res=''.join(wordfordiv,wordinrulesifx%div==0)returnresifreselsexdeffibo(x):a,b=1,1for_inrange(1,x):a,b=b,a+breturnbloop_over(range(20)).map(fibo).map(fizzbuzz).map(print).exhaust()Will produce the following output:112fizzbuzz813fizz34buzz89fizz233377buzzfizz159725844181fizzbuzz
Package: loopquest
:scroll:LoopquestA Production Tool for Embodied AI.:video_camera:Quickstart Demo,Dataset Demo:house:DiscordMajor featuresImitation Learning / Offline Reinforcement Learning Oriented MLOps. Log all the observation, action, reward, rendered images into database with only ONE extra line of code.env=gymnasium.make("MountainCarContinuous-v0",render_mode="rgb_array")->importloopquestenv=loopquest.make_env(gymnasium.make("MountainCarContinuous-v0",render_mode="rgb_array"))Directly trainable data for robotics foundation model. Select and download the (observation, action, reward) data with the dataloader interfaces of the most popular deep learning frameworks (e.g. tensorflow, pytorch, huggingface dataset apis). CheckDataset Quickstart Examplefor more details.fromloopquest.datasetsimportload_dataset,load_datasets# Load data from a single experimentds=load_dataset("your_experiment_id")# Load data from multiple experimentsds=load_datasets(["exp1","exp2"])The data schema will look like{'id':'34yixvic-0-1','creation_time':'2023-09-03T20:53:30.603','update_time':'2023-09-03T20:53:30.965','experiment_id':'34yixvic','episode':0,'step':1,'observation':[-0.55,0.00],'action':[0.14],'reward':-0.00,'prev_observation':[-0.55,0.00],'termnated':False,'truncated':False,'done':False,'info':'{}','sub_goal':None,'image_urls':['http://localhost:5667/api/step/34yixvic-0-1/image/0'],'images':[<PIL.JpegImagePlugin.JpegImageFileimagemode=RGBsize=600x400at0x7F8D33094450>]}All the regular MLOps features are included, e.g. data visualization, simulation rendering, experiment management.InstallationFor stable version, runpip install loopquestFor dev version or loopquest project contributors, clone the git to your local machine by runninggit clone https://github.com/LoopMind-AI/loopquest.gitChange to the project root folder and install the packagecd loopquest
pip install -e .UsageRunquickstart script,pythonexamples/quickstart.pyThe command prompt will ask you to select local or cloud instance. Pick the instance you want and once the script is up and running. You should see "Check your experiment progress onhttp://localhost:5667/experiment/<exp_id>orhttps://open.loopquest.ai/experiment/<exp_id" (depending on the instance you selected).Loopquest Developer Only: to bring up a development server that reflects your local changes in real time, runbashstart_dev_server.shQuick Start Exampleimportgymnasiumimportloopquestenv=loopquest.make_env(gymnasium.make("MountainCarContinuous-v0",render_mode="rgb_array"))obs,info=env.reset()foriinrange(100):action=env.action_space.sample()obs,reward,terminated,truncated,info=env.step(action)rgb_array=env.render()ifterminatedortruncated:breakenv.close()
Package: loop-rate-limiters
Loop rate limitersSimple loop frequency regulators in Python with an API similar torospy.Rate:fromloop_rate_limitersimportRateLimiterfromtimeimportperf_counterrate=RateLimiter(frequency=400.0)whileTrue:print(f"Hello from loop at{perf_counter():.3f}s")rate.sleep()A similarAsyncRateLimiterclass is available forasynchronous code.InstallationYou can install the library straight from PyPI:$pipinstallloop-rate-limitersOr equivalently from Conda:$condainstall-cconda-forgeloop-rate-limitersAsynchronous I/OTheAsyncRateLimiterclass provides a loop frequency limiter forasyncio:importasynciofromloop_rate_limitersimportAsyncRateLimiterasyncdefmain():rate=AsyncRateLimiter(frequency=400.0)whileTrue:loop_time=asyncio.get_event_loop().time()print(f"Hello from loop at{loop_time:.3f}s")awaitrate.sleep()if__name__=="__main__":asyncio.run(main())
Package: loopsapi
loopsapiPython wrapper for Loops.so APIInstallpip3 install loopsapiExampleimportosimportloopsapiAPI_KEY=os.environ['LOOPS_API_KEY']api=loopsapi.Loops(api_key=API_KEY)test=api.apikey.test()print(print)Classes and methodsLoops.apikey.test - Test if API KEY is valid

Loops.custom_fields.list - List custom fields

Loops.transactional_emails.send - Send transactional email

Loops.events.send - Send event

Loops.contacts.find - Find contact
Loops.contacts.create - Create contact
Loops.contacts.update - Update contact
Loops.contacts.delete - Delete contact
Package: loopsso
LoopsPyPI 0.0.2A convenient Python wrapper for theLoppsAPI.API docs:https://tryloops.notion.site/API-5b453a52dd7c4b419aa4647410de9770SetuppipinstallloopssoExamplesfrom loopsso import LoopsClient
client = LoopsClinet('YOUR-API-KEY')Contacts UsageCreate/Addclient.create( 
    email='adam@loops.so',
    firstName='Adam',
    lastName='Kaczmarek',
    favoriteColor='blue',
    userGroup='Founders',
    source='Signup form Service'
    )Updateclient.update( 
    email='adam@loops.so',
    firstName='Adam',
    lastName='Kaczmarek',
    favoriteColor='blue',
    userGroup='Founders',
    source='Signup form Service'
    )Findclient.find(email='adam@loops.so', user_id='SomeUserId')Deleteclient.delete(email='adam@loops.so')Sendclient.send(email='adam@loops.so', event_name='conversion')
Package: loop-step
SEAMM Loop Plug-inA SEAMM plug-in which provides loops in flowcharts.This plug-in provides looping control structures for flowcharts, like
afor loopin a programming language.Free software: BSD licenseDocumentation:https://molssi-seamm.github.io/loop_step/index.htmlCode:https://github.com/molssi-seamm/loop_stepFeaturesSimplefor i = # to # by #loopsForeachloop over elements in a listFor rowloop over rows in a table (see thetable plug-in)AcknowledgementsThis package was created withCookiecutterand themolssi-seamm/cookiecutter-seamm-pluginproject template.Developed by the Molecular Sciences Software Institute (MolSSI),
which receives funding from theNational Science Foundationunder
award OAC-1547580 and CHE-2136142.History2023.11.9 – Bugfix: “For” loops could crashFor loops could crash writing to write final_structure.mmcif before the directory
had been made.2023.10.30 – Improved consistency of printing loop information2023.4.24 – Better support for rerunning/restarting jobsRemove ‘iteration.out’ if it already exists.2023.2.15 – Starting to add loops over structures in the databaseReorganized the documentation and changed to the standard MolSSI theme.2022.1.16 – Improved error handlingWrote the traceback for any errors caught to item_nnn/stderr.out to aid debugging.2021.12.21 – Cleaned up the printing2021.12.12 – Error handling and querying for loops over tablesErrors in the body of the loop are caught and can be ignored or handled in different
ways at the direction of the user.When looping over rows in a table, a query can be used to select the rows to operate
on.2021.10.13 – updated to Python 3.8 and 3.92021.6.3 – updated for internal changes in argument parsing2021.2.11 (11 February 2021)Updated the README file to give a better description.Updated the short description in setup.py to work with the new installer.Added keywords for better searchability.2021.2.4 (4 February 2021)Updated for compatibility with the new system classes in MolSystem
2021.2.2 release.2020.12.4 (4 December 2020)Internal: switching CI from TravisCI to GitHub Actions, and in the
process moving documentation from ReadTheDocs to GitHub Pages where
it is consolidated with the main SEAMM documentation.2020.11.2 (2 November 2020)Updated to be compatible with the new command-line argument
handling.2020.10.1 (1 October 2020)Bugfix: fixed a blocking bug in handling of periodic system.2020.9.25.1 (25 September 2020)Internal: added a missed installation requirement.2020.9.25 (25 September 2020)Updated to be compatible with the new system classes in MolSystem.2020.8.3 (3 August 2020)Bugfix: corrected problem that caused nested loops to fail
sometimes.2020.7.0 (23 July 2020)Write the structure out at the end of each iteration so that it can
be viewed in the dashboard.0.9 (15 April 2020)Support for plots in the dashboard.0.7.0 (17 December 2019)General clean-up of code and output.0.6 (8 September 2019)Cleaned up description.Internal: preparing for using PyUp to check dependencies.0.2.0 (2019-07-29)First release on PyPI.0.1.0 (2019-01-13)First version created in GitHub.
Package: looptimer
looptimerProgress bar style loop timer with projected time-to-completion.Installationpip install looptimerUsageDirectly wrap any iterable withtimedloopto get iteration progress status:fromlooptimerimporttimedloopfor_intimedloop(range(100),label='foo'):...Sample outputOptional argumentsArgumentTypeDefaultDescriptionlabelstrNoneDescriptive label printed to the left of the progress barbar_charstr▢Character used to fill in the progress barbar_widthint20Character width of the progress baranimateboolTrueDisable to print each update on a new line (useful when interspersed with other log messages)max_refresh_secsfloat0.1Use to throttle print frequency for fast-iterating loops
Package: loopui
loopUITheloopUI Python packageprovides several functions to analyse the variability among an ensemble of voxets. It allows the computation of local measures of uncertainty (cardinality and entropy) and of global dissimilarity measures (e.g. based on multiple-point statistical analysis or wavelet decomposition among other possibilities).InstallationNote: package for python >=3.7To install the package:python -m pip install loopuiTo uninstall the package:python -m pip uninstall -y loopuiRequirementsThe following python packages are used by 'geone':matplotlibmpl_toolkitsnumpygzipscipysklearnpywtpandasExamplesA series ofPythonnotebooks demonstrate how to use the different functions.
First, runui-0-data.ipynbto load an ensemble of voxets. It is a pre-requisites prior running the other python notebooks.In order to compare the various indicator via the viewingui-9-comparison.ipynbnotebook, a prerequisite is to run the cardinality and entropy notebooks (other pre-computed indicators are saved in the pickledata folder, but the indicators can be computed by running the appropriate notebook).
Package: loopy
Loopy lets you easily generate the tedious, complicated code that is necessary
to get good performance out of GPUs and multi-core CPUs.
Loopy’s core idea is that a computation should be described simply and thentransformedinto a version that gets high performance. This transformation
takes place under user control, from within Python.It can capture the following types of optimizations:Vector and multi-core parallelism in the OpenCL/CUDA modelData layout transformations (structure of arrays to array of structures)Loop unrollingLoop tiling with efficient handling of boundary casesPrefetching/copy optimizationsInstruction level parallelismand many moreLoopy targets array-type computations, such as the following:dense linear algebra,convolutions,n-body interactions,PDE solvers, such as finite element, finite difference, and
Fast-Multipole-type computationsIt is not (and does not want to be) a general-purpose programming language.Loopy is licensed under the liberalMIT licenseand free for commercial, academic,
and private use. All of Loopy’s dependencies can be automatically installed from
the package index after using:pip install loopyIn addition, Loopy is compatible with and enhancespyopencl.—Places on the web related to Loopy:Python package index(download releases)Documentation(read how things work)Github(get latest source code, file bugs)HomepageBenchmarks
Package: loopyng
loopyingUtils to generate audioDepending on what you use, you might need some libraries such as:librosa
soundfile
pyttsx3 (for voiced_time.py)If you get an import error on X, just try to do:pip install Xin your terminal.If that doesn't work, google the library and see how to install it
For e.g. soundfile is actually obtained by doingpip install pysoundfileBuggers!
Package: loopytimer
Python looping timer. Forked fromterryltang/pythonTimerInstallpip install loopytimerFeature & UsageRequire Python standard library:threadingCall a function after a specified number of seconds:e.g.:
from loopytimer import LoopyTimer

timer = LoopyTimer(5.0, handler_func, args=[], kwargs={})
timer.set_call_limits(5)    # timer will run eternally if you don't set a limit
timer.start()
timer.cancel()     # cancel the timer's action if it's still waiting, will also destroy timer instanceTimer handler take positional arguments and keyword arguments:e.g.:
timer = LoopyTimer(0.5, timer_handler, args=["Terry", "Tang"], kwargs={"city":"Houston", "state":"Texas"})Cancel and destroy timer.Add a customized logger function for timer handler.Test cases included.LicenseMIT LicenseCopyright (c) 2018 Wavky HuangSee ->LICENSE
Package: loose-server
loose-serverLoose server is a simple configurable server. It can be used to create temporary servers, dynamically add or remove endpoints and set responses for them from an application.Installation$ python -m pip install loose-serverAdditional packages will be installed:Flask,flask-restful(required for the server) andrequests(required for the http clients).DocumentationDocumentation for the package can be found on theWiki.QuickstartLoose server has 2 variations:Standalone server with API to manage rules via HTTP.Flask application that can be used as a configurable mock in unit-tests.Standalone serverA server can be started by the command$ python -m looseserver.default.server.run
 * Serving Flask app "looseserver" (lazy loading)
 * Environment: production
   WARNING: Do not use the development server in a production environment.
   Use a production WSGI server instead.
 * Debug mode: off
 * Running on http://127.0.0.1:50000/ (Press CTRL+C to quit)API endpoints are nested to the base configuration url. By default it is/_configuration/.fromlooseserver.default.client.ruleimportPathRulefromlooseserver.default.client.responseimportFixedResponsefromlooseserver.default.client.httpimportHTTPClientclient=HTTPClient(configuration_url="http://127.0.0.1:50000/_configuration/")path_rule=client.create_rule(rule=PathRule(path="example"))json_response=FixedResponse(status=200,headers={"Content-Type":"application/json"},body='{"key": "value"}',)client.set_response(rule_id=path_rule.rule_id,response=json_response)All configured endpoints have a common prefix. By default it is/routes/.
The response can be obtained by the following request$ curl http://127.0.0.1:50000/routes/example -i
HTTP/1.0 200 OK
Content-Type: application/json
Content-Length: 16
Server: Werkzeug/0.15.2 Python/3.5.2
Date: Fri, 05 Apr 2019 14:08:50 GMT

{"key": "value"}Configurable mockLoose server can be used as a mock server in the following wayfromlooseserver.default.server.applicationimportconfigure_applicationfromlooseserver.default.client.flaskimportFlaskClientfromlooseserver.default.client.ruleimportPathRulefromlooseserver.default.client.responseimportFixedResponseapplication=configure_application(base_endpoint="/base/",configuration_endpoint="/config/")app_client=application.test_client()client=FlaskClient(configuration_url="/config/",application_client=app_client)path_rule=client.create_rule(rule=PathRule(path="example"))json_response=FixedResponse(status=200,headers={"Content-Type":"application/json"},body='{"key": "value"}',)client.set_response(rule_id=path_rule.rule_id,response=json_response)response=app_client.get("/base/example")assertresponse.headers["Content-Type"]=="application/json"assertresponse.json=={'key':'value'}
Package: looseversion
looseversion - Version numbering for anarchists and software realistsA backwards/forwards-compatible fork ofdistutils.version.LooseVersion,
for times when PEP-440 isn't what you need.The goal of this package is to be a drop-in replacement for the originalLooseVersion.
It implements an identical interface and comparison logic toLooseVersion.
The only major change is that alooseversion.LooseVersionis comparable to adistutils.version.LooseVersion, which means tools should not need to worry whether
all dependencies that use LooseVersion have migrated.If you are simply comparing versions of Python packages, consider moving topackaging.version.Version,
which followsPEP-440.LooseVersionis better suited to interacting with heterogeneous version schemes that
do not follow PEP-440.InstallationFrom PyPIpip install looseversionFrom sourcegit clone https://github.com/effigies/looseversion.git
pip install looseversion/Usage>>>fromlooseversionimportLooseVersion>>>LooseVersion("1.0.0")<LooseVersion("2.0.0")True>>>LooseVersion("1.0.0")<"2"True
Package: looter
Looter 🗡️A super-lightweight web-scraping tool.🏠HomepageInstallpipinstalllooterUsageUsage:lootergenspider<name>[--async]lootershell[<url>]looter(-h|--help|--version)Options:-h--helpShowthisscreen.--versionShowversion.--asyncUseasyncinsteadofconcurrent.Author👤alphardexGithub:@alphardexTwitter:@alphardex007Show your supportGive a ⭐️ if this project helped you!This README was generated with ❤️ byreadme-md-generator
Package: lootgenerator
Loot Generator 0.1.0Simple and plain console script for on-the-fly loot generation for tabletop RPGs.To install:
pip install lootgeneratorfrom loot_generator.tables import CSVLootTablefrom loot_generator.generators import BasicLootGeneratortable = CSVLootTable(‘my_loot_table_path.csv’)generator = BasicLootGenerator(table)generator.generate(1000)TBD: proper readme, examples, proper comments.
Package: loo-v2020
Loopy: Transformation-Based Generation of High-Performance CPU/GPU Code.. image::https://gitlab.tiker.net/inducer/loopy/badges/master/pipeline.svg:alt: Gitlab Build Status
:target:https://gitlab.tiker.net/inducer/loopy/commits/master.. image::https://github.com/inducer/loopy/workflows/CI/badge.svg?branch=master&event=push:alt: Github Build Status
:target:https://github.com/inducer/loopy/actions?query=branch%3Amaster+workflow%3ACI+event%3Apush.. image::https://badge.fury.io/py/loo.py.png:alt: Python Package Index Release Page
:target:https://pypi.org/project/loo.py/Loopy lets you easily generate the tedious, complicated code that is necessary
to get good performance out of GPUs and multi-core CPUs.
Loopy's core idea is that a computation should be described simply and thentransformedinto a version that gets high performance. This transformation
takes place under user control, from within Python.It can capture the following types of optimizations:Vector and multi-core parallelism in the OpenCL/CUDA modelData layout transformations (structure of arrays to array of structures)Loop unrollingLoop tiling with efficient handling of boundary casesPrefetching/copy optimizationsInstruction level parallelismand many moreLoopy targets array-type computations, such as the following:dense linear algebra,convolutions,n-body interactions,PDE solvers, such as finite element, finite difference, and
Fast-Multipole-type computationsIt is not (and does not want to be) a general-purpose programming language.Loopy is licensed under the liberalMIT license <http://en.wikipedia.org/wiki/MIT_License>_ and free for commercial, academic,
and private use. All of Loopy's dependencies can be automatically installed from
the package index after using::pip install loo.pyIn addition, Loopy is compatible with and enhancespyopencl <http://mathema.tician.de/software/pyopencl>_.Places on the web related to Loopy:Python package index <http://pypi.python.org/pypi/loo.py>_ (download releases) Note the extra '.' in the PyPI identifier!Documentation <http://documen.tician.de/loopy>_ (read how things work)Github <http://github.com/inducer/loopy>_ (get latest source code, file bugs)Wiki <http://wiki.tiker.net/Loopy>_ (read installation tips, get examples, read FAQ)Homepage <http://mathema.tician.de/software/loopy>_
Package: lopaths
An Algorithm to Compute Locally Optimal Paths for Large-Scale Traffic ModelsThis package provides an implementation of the algorithm REVC to compute
locally optimal routes between many origins and destinations, as described
byFischer et al. (2020). If you use this package
in research that leads to a scientific publication, we kindly ask you to
cite the publication.The package is licensed under theLGPL-v3. For documentation please refer tothe project's documentation page on github. Please report any bugs onthe project's bug tracker. You are welcome to adjust the provided
files to your needs and to improve the code. It is encouraged that you share any
improvements onthe github pageif possible.