Saravanan666/pypi-org huggingface dataset:
Package: deebot-client
Client Library for Deebot devices (Vacuums)InstallationIf you have a recent version of Python 3, you should be able to
dopip install deebot-clientto get the most recently released version of
this.UsageTo get started, you'll need to have already set up an EcoVacs account
using your smartphone.You are welcome to try using this as a python library for other efforts.
A simple usage might go something like this:importaiohttpimportasyncioimportloggingimporttimefromdeebot_client.api_clientimportApiClientfromdeebot_client.authenticationimportAuthenticator,create_rest_configfromdeebot_client.commandsimport*fromdeebot_client.commands.cleanimportCleanActionfromdeebot_client.eventsimportBatteryEventfromdeebot_client.mqtt_clientimportMqttClient,create_mqtt_configfromdeebot_client.utilimportmd5fromdeebot_client.deviceimportDevicedevice_id=md5(str(time.time()))account_id="your email or phonenumber (cn)"password_hash=md5("yourPassword")country="de"asyncdefmain():asyncwithaiohttp.ClientSession()assession:logging.basicConfig(level=logging.DEBUG)rest_config=create_rest_config(session,device_id=device_id,country=country)authenticator=Authenticator(rest_config,account_id,password_hash)api_client=ApiClient(authenticator)devices_=awaitapi_client.get_devices()bot=Device(devices_[0],authenticator)mqtt_config=create_mqtt_config(device_id=device_id,country=country)mqtt=MqttClient(mqtt_config,authenticator)awaitbot.initialize(mqtt)asyncdefon_battery(event:BatteryEvent):# Do stuff on battery eventifevent.value==100:# Battery fullpass# Subscribe for events (more events available)bot.events.subscribe(BatteryEvent,on_battery)# Execute commandsawaitbot.execute_command(Clean(CleanAction.START))awaitasyncio.sleep(900)# Wait for...awaitbot.execute_command(Charge())if__name__=='__main__':loop=asyncio.get_event_loop()loop.create_task(main())loop.run_forever()A more advanced example can be foundhere.Note for Windows usersThis library cannot be used out of the box with Windows due a limitation in the requirementaiomqtt.
More information and a workaround can be foundhereThanksMy heartfelt thanks to:deebotozmo, After all, this is a debotozmo fork :)sucks, deebotozmo was forked from it :)xmpppeek, a great library for examining XMPP traffic flows (
yes, your vacuum speaks Jabber!),mitmproxy, a fantastic tool for analyzing HTTPS,click, a wonderfully complete and thoughtful library for making Python command-line
interfaces,requests, a polished Python library for HTTP requests,Decompilers online, which was very helpful in figuring out what the Android app
was up to,Albert Louw, who was kind enough to post code
fromhis own experimentswith his device, andAll the users who have given useful feedback and contributed code!
Package: deebotozmo
=====Library for DeebotOzmo 960/950/920A simple command-line python script to drive a robot vacuum. Currently
known to work with the Ecovacs Deebot 960/950/920 from both North America and Europe.InstallationIf you have a recent version of Python 3, you should be able to
dopip install deebotozmoto get the most recently released version of
this.UsageTo get started, you'll need to have already set up an EcoVacs account
using your smartphone.You are welcome to try using this as a python library for other efforts.
A simple usage might go something like this:importaiohttpimportasyncioimportloggingimportrandomimportstringfromdeebotozmo.commandsimport*fromdeebotozmo.commands.cleanimportCleanActionfromdeebotozmo.ecovacs_apiimportEcovacsAPIfromdeebotozmo.ecovacs_mqttimportEcovacsMqttfromdeebotozmo.eventsimportBatteryEventfromdeebotozmo.utilimportmd5fromdeebotozmo.vacuum_botimportVacuumBotdevice_id="".join(random.choice(string.ascii_uppercase+string.digits)for_inrange(12))email="your email or phonenumber (cn)"password_hash=md5("yourPassword")continent="eu"country="de"asyncdefmain():asyncwithaiohttp.ClientSession()assession:logging.basicConfig(level=logging.DEBUG)api=EcovacsAPI(session,device_id,email,password_hash,continent=continent,country=country,verify_ssl=False)awaitapi.login()devices_=awaitapi.get_devices()auth=awaitapi.get_request_auth()bot=VacuumBot(session,auth,devices_[0],continent=continent,country=country,verify_ssl=False)mqtt=EcovacsMqtt(continent=continent,country=country)awaitmqtt.initialize(auth)awaitmqtt.subscribe(bot)asyncdefon_battery(event:BatteryEvent):# Do stuff on battery eventifevent.value==100:# Battery fullpass# Subscribe for events (more events available)bot.events.battery.subscribe(on_battery)# Execute commandsawaitbot.execute_command(Clean(CleanAction.START))awaitasyncio.sleep(900)# Wait for...awaitbot.execute_command(Charge())if__name__=='__main__':loop=asyncio.get_event_loop()loop.create_task(main())loop.run_forever()A more advanced example can be foundhere.ThanksMy heartfelt thanks to:sucks, After all, this is a sucks fork :)xmpppeek, a great library for examining XMPP traffic flows (
yes, your vacuum speaks Jabbber!),mitmproxy, a fantastic tool for analyzing HTTPS,click, a wonderfully complete and thoughtful library for making Python command-line
interfaces,requests, a polished Python library for HTTP requests,Decompilers online, which was very helpful in figuring out what the Android app
was up to,Albert Louw, who was kind enough to post code
fromhis own experimentswith his device, andAll the users who have given useful feedback and contributed code!
Package: deebot-t8
Deebot T8Yet another Deebot client library, intended to bring better support to Ozmo T8 range of robots.This library is used bymy Home Assistant custom componentfor the Deebot T8Supported RobotsEcovacs Deebot Ozmo T8 (Model: OT8G)Other Deebot Ozmo T8 variant robots will likely also work, but have not been tested. Please open an issue if you have success with other models 😊Models can be listed using the CLI:deebot-t8 list-devicesFeaturesAlways up to date state using Ecovacs MQTT serviceFully featured command line interfaceExtenable interface, with abstractions hiding ugly HTTP necessitiesInstallTo install the base library for use in other Python projects:pip install deebot-t8To install the base library, as well as the CLI dependencies:pip install deebot-t8[cli]The package exposes the cli as adeebot-t8executable binary.$>deebot-t8--help
Usage:deebot-t8[OPTIONS]COMMAND[ARGS]...

Options:--config-filePATH--helpShowthismessageandexit.

Commands:devicelist-devicesloginrenew-access-tokensSpecial Thanksmitmproxyfor reverse engineering HTTP(s) calls from the iOS apphttps://github.com/And3rsL/Deebotozmoandhttps://github.com/wpietri/sucksfor protocol details for authenticationhttps://github.com/Ligio/ozmofor inspiring and guiding the MQTT subscription implementationContributingI am more than happy to receive pull requests to add support for missing or broken features. Bug reports via issues also welcome, however is no guarantee of the bug being resolvedAs this library is intended to be narrow focused on the Deebot T8 line, depending on the complexity burden, pull requests to support additional robots may be rejected.
Package: deed
deed - PythonActivityFormatter for object changes (auditlog)UsageTry it on your python console:fromdeedimportAuditLog,AuditMode# stored resourceresource={'_id':'123','user_id':'123','customer_id':'123','title':"My amazing post"}refs=[# stakeholders'post:'+resource['_id'],'owner:'+resource['user_id'],'customer:'+resource['customer_id'],]# update exampleaudit_log=AuditLog(resource_type='shipment',action='update',audit_mode=AuditMode.DIFF)audit_log.session(actor='user:admin@example.com',where='10.0.0.2',channel='edit-form',stakeholders=refs)patch={'title':'My first post'}new_resource={**resource,**patch}audit_log.audit(resource,payload=new_resource)Bottle Integrationfrombottleimportrun,request,BottlefromdeedimportAuditModefromdeed.bottle_kitimportaudit,DeedPluginfrombusiness.modelsimportPostapi=Bottle()api.install(DeedPlugin())@api.put('/post/<id:int>')@audit(resource_type='post',action='update',audit_mode=AuditMode.DIFF)defupdate_post(id:int):stored_post=Post.find(id)payload=request.jsonupdated_post={**stored_post,**payload}Post.update(updated_post)request.audit.session['actor']='admin'# authorized userrequest.audit.resource=stored_postrequest.audit.payload=updated_postreturnpostif__name__=='__main__':run(api,port=8080)
Package: deedee
Dependency inversion for python, the easy wayNutshellDeedee provides an API which enables the developer to use late binding for
default values in function definition.Example:importdeedeeclassDatabase:defselect(self,query):print("select",query)@deedee.resolvedefhandler(param_a,param_b,database=deedee.context.database):print(param_a,param_b)# param_a and param_b are unchangedprint(database)# this will print a database instancedatabase.select("select * from table order by id")# this call will be successfuldefmain():# register the default value (which will be used when calling handler)deedee.context.register("database",Database())# call the handler, the 3rd parameter will be the previously registered valuehandler("param_1","param_2")if__name__=='__main__':main()In python the binding of the default value happens at the function
defintion, so it is not possible to set the default parameter later on, the
expression after the ‘=’ sign gets evaluated at the function definition.Withdeedeethis changes, so you can specify a reference instead, and later on, specify the value for that reference.When the function is called later on, the decoratordeedee.resolvewill
resolve the reference of the default value.This can be used to implement dependency injection and dependency inversion
patterns without using any boilerplate classes or functions.FeaturesLazy evaluation of the default parametersDefault value can be overriden by specifying it during the call (same behaviour as ‘normal’ default values)It can be traced which function references to a given default parameter (soon)It can be checked that all references can be resolved in the future, so it
can be ensured that there will be no error with resolving during function
callsContext objects can be implemented by the user (it is not required to use deedee.context)Annotations can be use freely, the library doesn’t use or change themNearly straightforward implementation, good test coverageNameAccording to urban dictionary, deedee is:A person who is always there when you need them. A strong survivor who can make it through anything.In deedee the values you are registering to a context behave like this – they are always there when you need them.
Package: deelight
yeelight
xiaomi
iot
home-automation
upnp
asyncio
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Framework :: AsyncIO
Classifier: Topic :: Home Automation
Package: deel-lip
Explore DEEL-LIP docs »👋 Welcome to deel-lip documentation!Controlling the Lipschitz constant of a layer or a whole neural network
has many applications ranging from adversarial robustness to Wasserstein
distance estimation.This library provides an efficient implementation ofk-Lispchitz
layers for keras.📚 Table of contents📚 Table of contents🔥 Tutorials🚀 Quick Start📦 What's Included👍 Contributing👀 See Also🙏 Acknowledgments🗞️ Citation📝 License🚀 Quick StartYou can installdeel-lipdirectly from pypi:pipinstalldeel-lipIn order to usedeel-lip, you also need avalid tensorflow
installation.deel-lipsupports tensorflow versions 2.x.🔥 TutorialsTutorial NameNotebookGetting Started 1 - Creating a 1-Lipschitz neural networkWasserstein distance estimation on toy exampleHKR Classifier on toy datasetHKR classifier on MNIST datasetHKR multiclass and fooling📦 What's Includedk-Lipschitz variant of keras layers such asDense,Conv2DandPooling,activation functions compatible withkeras,kernel initializers and kernel constraints forkeras,loss functions that make use of Lipschitz constrained networks (seeour paperfor more
information),tools to monitor the singular values of kernels during training,tools to convert k-Lipschitz network to regular network for faster
inference.👍 ContributingTo contribute, you can open anissue, or fork this
repository and then submit changes through apull-request.
We useblackto format the code and follow PEP-8 convention.
To check that your code will pass the lint-checks, you can run:tox-epy36-lintYou needtoxin order to
run this. You can install it viapip:pipinstalltox👀 See AlsoMore from the DEEL project:Xpliquea Python library exclusively dedicated to explaining neural networks.InfluenciaePython toolkit dedicated to computing influence values for the discovery of potentially problematic samples in a dataset.deel-torchlipa Python library for training k-Lipschitz neural networks on PyTorch.DEEL White papera summary of the DEEL team on the challenges of certifiable AI and the role of data quality, representativity and explainability for this purpose.🙏 AcknowledgmentsThis project received funding from the French ”Investing for the Future – PIA3” program within the Artificial and Natural Intelligence Toulouse Institute (ANITI). The authors gratefully acknowledge the support of theDEELproject.🗞️ CitationThis library has been built to support the work presented in the paperAchieving robustness in classification using optimaltransport with
Hinge regularizationwhich aim
provable and efficient robustness by design.This work can be cited as:@misc{2006.06520,
    Author = {Mathieu Serrurier and Franck Mamalet and Alberto González-Sanz and Thibaut Boissin and Jean-Michel Loubes and Eustasio del Barrio},
    Title = {Achieving robustness in classification using optimal transport with hinge regularization},
    Year = {2020},
    Eprint = {arXiv:2006.06520},
}📝 LicenseThe package is released underMIT license.
Package: deemix
deemixWhat is deemix?deemix is a deezer downloader built from the ashes of Deezloader Remix. The base library (or core) can be used as a stand alone CLI app or implemented in an UI using the API.InstallationNOTE: Ifpython3is "not a recognized command" try usingpythoninstead.From PyPiYou can install the library by usingpip:python3 -m pip install deemixIf you install it this way you can use the deemix CLI by usingdeemixdirectly in your terminal instead ofpython3 -m deemixBuilding from sourceAfter installing Python open a terminal/command prompt and install the dependencies usingpython3 -m pip install -r requirements.txt --userRunpython3 -m deemix --helpto see how to use the app in CLI mode.What's left to do?Write the API DocumentationFix whatever is brokenLicenseThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, seehttps://www.gnu.org/licenses/.
Package: deemon
About|Installation|Docker|Documentation|SupportAboutdeemon is a command line tool written in Python that monitors artists for new releases, provides email notifications and can also integrate with the deemix library to automatically download new releases.SupportOpen an Issue|DiscordInstallationUsing pip$pipinstalldeemonFrom source$pipinstall-rrequirements.txt
$python3-mdeemonDockerDocker support has been added foramd64,arm64andarmv7architectures. It is recommended to save yourdocker runcommand as a script to execute via cron/Task Scheduler.Note:Inside deemon'sconfig.json, download_locationmustbe set to/downloadsuntil I can integrate this myself.Example: Refreshing an existing databasedocker run --name deemon \
       --rm \
       -v /path/to/deemon/config:/config \
       -v /path/to/music:/downloads \
       -v /path/to/deemix/config:/deemix  \
       ghcr.io/digitalec/deemon:latest \
       python3 -m deemon refreshInstallation in a Python Virtual Environment (venv)If you wish to install deemon and it's dependencies in a sandbox-style environment, I would recommend using venv.Create a venv and install deemon (you may need to usepython3andpip3depending on your system):$ python -m venv venv
$ source ./venv/bin/activate
$ pip install deemonWhen you are finished, close the terminal or exit our venv:$ deactivateNext time you want to run deemon, activate the venv first:$ source ./venv/bin/activate
$ deemon refreshIf you are moving to venv from the Docker container, be sure to update your cron/Task Scheduler scripts.Default ConfigurationIf you need to generate a new default configuration, please rename or delete your currentconfig.json. The
configuration will be generated the next time you run deemon.
Package: deen
deenAn application that allows to apply encoding, compression and hashing to generic input data. It is meant to be a handy tool for quick encoding/decoding tasks for data to be used in other applications. It aims to be a lightweight alternative to other tools that might take a long time to startup and should not have too many dependencies. It includes a GUI for easy interaction and integration in common workflows as well as a CLI that might be usefule for automation tasks.Installationpip3installdeenFurther information is available in thewiki.PackagesThere is adeen-gitpackage available in the Arch User Repository (AUR).UsageSee thewikifor basic and more advanced usage examples.Bash CompletionBash completion can be enabled by adding the following line to your~/.bash_profileor.bashrc:source ~/path/to/deen-completion.shZSH CompletionZSH completion can be enabled by adding the following line to your~/.zshrc:autoload bashcompinit && bashcompinit && source ~/path/to/deen-completion.sh
Package: deep
Flexible comparison of nested python datastrucures and objects.Composable "regular expressions" for data structures.This module allows easy and flexible deep comparisons ofdatastructure. It's not well documented yet but it's a port to pythonof Test::Deep which is well documented(http://search.cpan.org/~fdaly/Test-Deep/).It can provide useful output, pin-pointing where 2 items begin todiffer, rather than just returning true or false. This makes it veryuseful for unit-testing and deep.test provides classes for use withunittest.py .It allows easy comparison of objects - it just checks thatthey're in the same class and have the same __dict__.It allows set-wise comparison of lists or tuples.Most importantly it allows aribitrary nesting of the variouscomparisons and embedding of them inside other datastructures andobject. This makes it easy to perform comparisons that would otherwisehave been tedious.It turns what would have involved lots of looping and iffing andcomparing into just putting together a structure that looks like whatyou're trying to match.Examples:#### Test that we have a list of objects from the correct classes (yes you# can do this easily without deep.py).three_types = [deep.InstanceOf(int),deep.InstanceOf(list),deep.InstanceOf(dict)]diff = deep.diff([1, 2, 3], three_types)if diff:diff.print_full()# Outputsx[1]:Expected: instance of <type 'list'>Actual  : instance of <type 'int'>#### Test that we have a list of strings that all match a pattern.list_of_bings = deep.Elements(deep.Re("^bing "))diff = deep.diff(["bing bong", "bing crosby", "bin laden"], list_of_bings)if diff:diff.print_full()# Outputsx[2]:Expected: something matching '^bing 'Actual  : 'bin laden'#### Test that we have an object who's "wibble" attribute is a dict and# that this dict has 3 keys "time", "bings" and "things" and# "time". We want to make sure that "time" is a float and we want to# reuse the comparisons from the earlier examples for the other 2# keys.complex = deep.Attr("wibble", {"time": deep.InstanceOf(float),"bings": list_of_bings,"things": three_types})class O(object):wibble = {"time": 1234.12,"bings": ["bing go"],"things": [1, [], {}, "hello"]}diff = deep.diff(O(), complex)if diff:diff.print_full()# Outputslen(x.wibble['things']):Expected: 3Actual  : 4
Package: deep4dream
Deep Dream generator in Keras by using Inceptionv3 modelInstallationpip install deep4dream
or in colab google cloud
!pip install deep4dreamUsagefrom deep4dream import deep_dream
image_path='sky.jpg' #input image
new_dream='dream_sky' #result_prefix
iterations_no=200  #Number of ascent steps per scale
deep_dream.deep_dream_image(image_path,new_dream,iterations_no)Specific iterations_no Number of ascent steps per scale Selection
Package: deep500
Deep500: A Deep Learning Meta-Framework and HPC Benchmarking Library(or: 500 ways to train deep neural networks)Deep500 is a library that can be used to customize and measure anything with deep neural networks, using a clean, high-performant, and simple interface. Deep500 includes four levels of abstraction: (L0) Operators (layers); (L1) Network Evaluation; (L2) Training; and (L3) Distributed Training.Using Deep500, you automatically gain:Operator validation, including gradient checking for backpropagationStatistically-accurate performance benchmarks and plotsHigh-performance integration with popular deep learning frameworks (see Supported Frameworks below)Running your operator/framework/optimizer/communicator/... with real workloads, alongside existing environmentsand much more...InstallationUsing pip:pip install deep500UsageSee thetutorials.RequirementsPython 3.5 or laterProtobuf (sudo apt-get install protobuf-compiler libprotoc-dev)For plotted metrics: matplotlibFor distributed optimization:Any MPI implementation (OpenMPI, MPICH, MVAPICH etc.)mpi4py Python packageSupported FrameworksTensorflowPytorchCaffe2ContributingDeep500 is an open-source, community driven project. We are happy to accept Pull Requests with your contributions!LicenseDeep500 is published under the New BSD license, seeLICENSE.