Saravanan666/pypi-org huggingface dataset:
Package: adsms
adsmsSend SMS aircraft alerts based on ADS-B dataUsageCopy the configuration file, make any necessary changes, and run:./adsms.py <configuration_file>Configuration filetextbelt_key: yourTextbeltAPI keydata: a URL to a readsb/tar1090aircraft.jsonendpointtracker: a URL to a tar1090 tracker (e.g.https://globe.theairtraffic.com/)database: an SQLite file in which to store subscriptionspid_file: path to which to write the PID (set to empty string to not write
a PID file)max_age: maximum age of aircraft pings in seconds; pings older than this
will be ignoredmin_disappearance: the minimum time in seconds for which an aircraft must
go "off the radar" before disappearing for new pings to trigger notifications
againdelay: time to wait after processing all rules before running the loop
againDatabase Schemaadsmsuses a SQLite database to store subscriptions and information about tracked aircraft. Currently, the only table issubscriptions.subscriptionsThesubscriptionstable has the following columns:Column NameData TypeDescriptionrowidINTEGERUnique identifier for the subscription.phoneTEXTPhone number to receive notifications for this subscription.icaoTEXTICAO address of the aircraft to track.descriptionTEXTDescription of the aircraft being tracked.last_seenINTEGERTimestamp of the last time this aircraft was seen by the system.This table stores information about each subscription, including the phone number to send notifications to, the ICAO address of the aircraft to track, a description of the aircraft, and the last time it was seen by the system.Use of ChatGPTPortions of both this README and theadsmscode have been partially written with ChatGPT.
Package: adsmt
Simple Active Directory console-based management toolkit (CLI - command line interface) which is designed to execute basic administration of MSAD like creating, modifying, listing, deleting and searching users, groups, computers; printing and managing domain tree structure and so on. The point of this toolkit creation was to give to system administrators an ability to easily manage Samba4-based AD domains (when there is no ability to use GUI MS management console and samba utilities (samba-tool, ldbsearch and so on) are not comfortable to use.
Package: adso
A topic modelling library built on top of scipy/numpy and nltk.installTo install:pip install adsoconfigadso need to write some files to disk.
As default adso uses the~/.adsofolder, but it can be change setting the enviromental variableADSODIR.documentationDocumentation with examples is hosted onGitHub PagesSome examples on how to use adso are also available intestsandexamplesfolders.
Package: adsocket
ADSocketWebSocket server based onaiohttp.InstallUsing python packagepipinstalladsocketHow it worksTo start the very basic server, this command will do:adsocketassuming that you have redis server started on localhost listening on port 6379.Now you should be able to connect to server on ws://localhost:5000Basic usageAdsocket should work out of the box, however it's probably not what you would expect.
To customize adsocket you can create custom channels, authentication or commands.Example of setting filefromadsocket.conf.default_settingsimport*# NOQACHANNELS={'global':{'driver':'my_package.channels.GlobalChannel','create_on_startup':True,},'user':{'driver':'my_package.channels.MyUserChannel','create_on_startup':False,}}AUTHENTICATION_CLASSES=('sraps_socket.auth.SrapsAuth',)DISCONNECT_UNAUTHENTICATED=FalseTo apply changes to adsocket you need to export environment variable with
path to settingsexportADSOCKET_SETTINGS=my_package.settingsSending messages from you applicationSeeadsocket-transport.Documentation@TodoGoalsOut motivation to behind is follows:High scalabilityHigh performanceEasy customizationEasy extendabilityChannelsAll communication between server and client is through channels.
Any client (understand websocket connection) can be member of n channels.
There is no automatic subscription to channel so in order ot receive messages
from server client have to subscribe to channels he or she wants to receive
messages from or publish messages to.Custom channels@TodoCommands@TodoCustom command@Todo
Package: adsocket-transport
Adsocket transportInstallpipinstalladsocket-transportUsageTransport initialization and sending message is very simplefromadsocket_transportimportADSocketTransportadsocket=ADSocketTransport(driver='redis',host='redis://localhost:6379',db=1)adsocket.send_data(data={'obj':'user','obj_id':4},channels={'name':'global','id':'global'})in case of async transportfromadsocket_transportimportADSocketAsyncTransportadsocket=ADSocketAsyncTransport(driver='redis',host='redis://localhost:6379',db=1)awaitadsocket.send_data(data={'obj':'user','obj_id':4},channels={'name':'global','id':'global'})Alternatively you can create message manuallyfromadsocket_transportimportMessage,ADSocketAsyncTransportadsocket=ADSocketAsyncTransport(driver='redis',host='redis://localhost:6379',db=1)message=Message(type='publish',data={'obj':'user','obj_id':4},channel='global',channel_id='global')awaitadsocket.send(message)For more seeadsocket-transport.
Package: adsocket-transport-django
Adsocket transportInstallpipinstalladsocket-transport-djangoUsageUsing django.db.singals is very easy...fromdjango.appsimportAppConfigfromadsocket_transport_django.appsimportADSocketConfigclassVideosConfig(ADSocketConfig,AppConfig):"""Basic application config"""name="myapp"verbose_name="My App"adsocket_signals=['myapp.ws_message_creator.VideoMessageCreator']fromadsocket_transport_django.creatorimportADSocketCreatorfromadsocket_transport_djangoimportCREATE,UPDATE,DELETE,MessagefrommyappimportmodelsclassVideoMessageCreator(ADSocketCreator):classMeta:model=models.Tododefcreate(self,model):returnMessage(type='publish',channel=f'todos:{model.pk}',data={'obj':model.pk,'action':'create'})defupdate(self,model):returnMessage(type='publish',channel=f'todos:{model.pk}',data={'obj':model.pk,'action':'update'})defdelete(self,model):returnMessage(type='publish',channel=f'todos:{model.pk}',data={'obj':model.pk,'action':'delete'})
Package: adsorb
Lightweight loose coupling library for Python. Add listeners to a named event
and call them from anywhere to collect their responses.Event listeners are called in an undefined sequence and all responses are
collected before execution resumes with the caller. Future versions may
add support to call listeners in parallel threads.InstallationRuneasy_install adsorborpip install adsorbto get fromPyPI(not yet implemented).To install from source, runpython setup.py install.To run the tests, installnoseandcoverageand runpython setup.py nosetests.UsageSeehttp://jace.github.com/adsorb/0.1Initial version
Package: adsorption-file-parser
Adsorption File ParserA pure python parser to sorption files from various instrumentation manufacturers.
It comes with minimal dependencies and maximum flexibility.Currently supports files from:Micromeritics (‘.xls’ reports)Surface Measurement Systems DVS (‘.xlsx’ reports)3P instruments (‘.xlsx’ reports)Quantachrome (‘.txt’ raw isotherm data)MicrotracBEL (‘.dat’, ‘.xls’ and ‘.csv’ files)statuslicensetestspackageInstallationInstall using pippipinstalladsorption-file-parserDocumentationThe main read function returns two dictionaries:
ametadictionary, which contains various metadata
that is present in the report (date, user, units)
and thedatadictionary, containing lists
of individual isotherm data.fromadsorption_file_parserimportreadmeta,data=read(path="path/to/file",manufacturer="manufacturer",fmt="supported format")Bugs or questions?For any bugs found or feature requests, please open anissueor, even better, submit apull request.
Package: adspower
adspowerThe package for interacting with API of anti-detect browser AdsPower.Telegram channelBug reportsadspower's source code is made available under theMIT LicenseRestrictionsDuring using the package, AdsPower must be opened.The local API is available only in paid AdsPower subscriptionsAdsPower has frequency control for all APIs, max. access frequency: 1 request/secondQuick startFor example, you can use driver by the following way.fromadspowerimportAdsPowerads_power=AdsPower('ja54rwh')withads_powerasdriver:driver.get('https://github.com/blnkoff/adspower')Here is example of usage of creating profiles from proxiesfromadspowerimportAdsPower,CreateProfileParams,UserProxyConfigdefcreate_profiles(config:dict[str],proxies)->list[AdsPower]:group_name=config['ads_power']['group_name']group_id=AdsPower.query_group(group_name=group_name)[0]['group_id']data_set=[]forproxyinproxies:proxy_config=UserProxyConfig(proxy_soft='other',proxy_type=proxy.type,proxy_host=proxy.host,proxy_port=proxy.port,proxy_user=proxy.login,proxy_password=proxy.password)data_set.append(CreateProfileParams(group_id=group_id,user_proxy_config=proxy_config))profiles=[]fordataindata_set:profiles.append(AdsPower.create_profile(data))returnprofilesHere is example of retrieving profiles from AdsPowerfromadspowerimportAdsPowerdefretrieve_profiles(config:dict[str]):group_name=config['ads_power']['group_name']group_id=AdsPower.query_group(group_name=group_name)['group_id']response=AdsPower.query_profiles(group_id=group_id)profiles=[AdsPower(profile['user_id'])forprofileinresponse]returnprofilesInstalling adspowerTo install the package from GitHub you can use that:pipinstallgit+https://github.com/blnkoff/adspower.gitTo install the package from PyPi you can use that:pipinstalladspower
Package: adspy
pyads is a simple way of interacting with a bibtex library built primarily fromthe ADS (http://adsabs.harvard.edu/abstract_service.html). Your mileage mayvary, but I've found it useful for quickly looking up cite info.  There are alot of improvements that could be made.Here's a short script to start showing the functionality.  I'll try to get getsome more docs up here soon.lib = ADSLibrary('test')for t in ["2011ApJ...735...96S", "2008ApJ...689.1063S"]:lib.add_entry(t)lib.write_bib()lib.save()lib.find('Skillman')lib.find('Dinosaur')lib.find('2008ApJ...689.1063S')b = lib.find('Skillman2011')b.download() # Not fully testedb.open() # Also not fully tested.# You would import an exisiting bib library with:# lib.import_bib('library.bib')# lib.save()nn
Package: adspygoogle
Notice: This library is now sunsetAs of 1/5/15, this library is sunset and will no longer receive updates for new
versions of supported APIs or bug fixes. Once all supported APIs are no longer
compatible with this library, it will be removed from GitHub and PyPI.A newer library named googleads is available that supports Python 2.7 and Python
3.3+. You can read more about it here:The release announcement.googleads github page.Migrating from adspygoogle to googleads.The Google Ads APIs Python Client LibrariesThe Google Ads APIs Python Client Libraries support the following products:AdWords API Python Client Library v15.17.1DoubleClick for Advertisers API Python Client Library v2.4.2DFP API Python Client Library v9.12.0You can find more information about the Google Ads Python Client Librarieshere.InstallationYou have two options for installing the Ads Python Client Libraries:Install with a tool such as pip:$ sudo pip install adspygoogle
$ --allow-external PyXML
$ --allow-unverified PyXML
$ --allow-external ElementTree
$ --allow-unverified ElementTree
$ --allow-external cElementTree
$ --allow-unverified cElementTreeInstall manually after downloading and extracting the tarball:$ sudo python setup.py installExamples and Configuration ScriptsThis package only provides the core components necessary to use the client
libraries. If you would like to obtain example code for any of the included
client libraries, you can find it on ourdownloads page.Known IssuesDue to changes to PyPI’s installation process, using ‘pip install’ to install
the library currently requires a number of ‘allow-external’ and
‘allow-unverified’ flags for the external dependencies of PyXML, ElementTree,
and cElementTree. If you’re using a version of Python greater than 2.5 and
would prefer not to install PyXML or cElementTree from external sources, then
follow the steps below to install via the tarball without those dependencies
(cElementTree is installed already as a default library in > Python 2.5).
Alternatively, if you would prefer to download these libraries to install
yourself, they can be downloaded at these locations:ElementTreecElementTreePyXMLThe installation of PyXML and cElementTree will fail on Ubuntu 13.04. If you
are trying to install adspygoogle on Ubuntu 13.04, you should avoid installing
these dependencies. If you need to use either of these dependencies, there is
a work-around that can be found inthis bug.
Another alternative is to install manually and exclude these dependencies. To
do so, first download and extract the tarball below. In the root directory,
run the following command:$ sudo python setup.py install --no_PyXML --no_cElementTreeContact UsDo you have an issue using the Ads Python Client Libraries? Or perhaps some
feedback for how we can improve them? Feel free to let us know on ourissue tracker.
Package: ads-pytest-parser
Pytest Parser to Azure DevOps Test CaseAssociate DevOps test cases with pytest nunit XML resultsUsageThe parser should be run on an Azure DevOpspytest-azurepipelinesgenerated XML file, because it searches for the following attributes:"test-case""methodname""result"The Python script requires the following arguments:org: Azure DevOps organization nameproject: Azure DevOps project nameplan: Azure DevOps test plan namesuite: Azure DevOps test suite nameauth: Azure DevOps personal access tokenxml: pytest-azurepipelines generated XML fileThe test cases should be named in the following format:testcase_<DevOpsID>, i.e.def testcase_123456():For example:ads-pytest-parser org project planID suiteID authToken test.xmlThe script will then associate the test cases with the test results - for the test cases the outcome will be set.
Package: adt-extension
adt-extensionPython abstract data structure (ADT) extension.Install:pipinstalladt-extensionImport:fromadt_extensionimportSet,SwitchDictExtensionsCurrently the package have the ADT extensions:ClassExtension ofDescriptionSetsetSet with element type and validation rule.SwitchDictdictDictionary with the possibility of behavior of aswitch case.SetSet with element type and validation rule for the elements that will be inserted into the set.Example:fromadt_extensionimportSet# A set with only even integersset_int_even=Set(element_type=int,rule=lambdax:(x%2==0))# Elements that satisfies the element type and validation ruleset_int_even.update({4,6})# Elements that satisfies the element type, but not validation ruleset_int_even.update({5})# Elements that not satisfies the element typeset_int_even.update({"qwe",True})print(set_int_even)# Output:# Set({4, 6})# Remove element typeset_int_even.element_type=None# Remove validation ruleset_int_even.rule=NoneSwitchDictDictionary with the possibility to perform a function or return a value when trying to access a nonexistent index in the dictionary class.Example:fromadt_extensionimportSwitchDict# Same behavior of a normal dictionaryswitch_dict=SwitchDict({'Apartament':125,'House':250,'Condominium':300,})# Add default caseswitch_dict.default_case=999# List exampleproperties_list=['Apartament','House','Condominium','Treehouse','Hotel',]# Get valuesproperties_values=[switch_dict[x]forxinproperties_list]print(properties_values)# Output:# [ 125, 250, 300, 999, 999 ]# Remove default case, becomes a normal dictionaryswitch_dict.default_case=None
Package: adtk
Anomaly Detection Toolkit (ADTK)Anomaly Detection Toolkit (ADTK) is a Python package for unsupervised /
rule-based time series anomaly detection.As the nature of anomaly varies over different cases, a model may not work
universally for all anomaly detection problems. Choosing and combining
detection algorithms (detectors), feature engineering methods (transformers),
and ensemble methods (aggregators) properly is the key to build an effective
anomaly detection model.This package offers a set of common detectors, transformers and aggregators
with unified APIs, as well as pipe classes that connect them together into
models. It also provides some functions to process and visualize time series
and anomaly events.Seehttps://adtk.readthedocs.iofor complete documentation.InstallationPrerequisites: Python 3.5 or later.It is recommended to install the most recentstablerelease of ADTK from PyPI.pipinstalladtkAlternatively, you could install from source code. This will give you thelatest, but unstable, version of ADTK.gitclonehttps://github.com/arundo/adtk.gitcdadtk/
gitcheckoutdevelop
pipinstall./ExamplesPlease seeQuick Startfor a simple example.For more detailed examples of each module of ADTK, please refer toExamplessection in the documentation oran interactive demo notebook.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 unit tests as appropriate.Please seeContributingfor more details.LicenseADTK is licensed under the Mozilla Public License 2.0 (MPL 2.0). See theLICENSEfile for details.
Package: adtrees
adtreesimplements some methods for qualitative and quantitative evaluation of security usingattack treesandattack-defense trees.The package is intended to be used together with theADTool, but this is not obligatory.PrerequisitesOptimization problems on attack-defense trees are solved usinglp_solve. For the installation oflp_solve, seeUsing lpsolve from Python. General information onlp_solvecan be foundhere.No special prerequisites for the remaining functionalities ofadtrees.Installationpip install adtreesExampleimportadtreesasadt# initialize attack(-defense) tree from an output file 'tree.xml' produced by the ADToolT=adt.ADTree('tree.xml')# create a basic assignment of cost for the basic actions of the defender in Tba=adt.BasicAssignment()forbinT.basic_actions('d'):ba[b]=10# create an instance of the 'maximal coverage' optimization problemproblem=adt.ADTilp(T,costassignment=ba,budget=100,problem='coverage')# solve the problemproblem.solve()# the optimal set of countermeasures and some additional information is displayedFor other functionalities and more details, refer to the walk-through examples inexamples folder.
Package: adtypingdecorators
StatusCompatibilitiesContactAdTypingDecoratorsPython decorators allowing to check and/or enforce types in functions' arguments based on typing hints.InstallationpipinstalladtypingdecoratorsUsageimportnumpyasnpimportpandasaspdfromadtypingdecoratorsimporttyping_raise,typing_convert,typing_warn,typing_customdefto_array(a:int):returnnp.array([a,2*a])defto_array_2(a:int):returnnp.array([a,3*a])@typing_raisedeff_raise(a:int):returna+1@typing_convertdeff_convert(a:int):returna+1@typing_warndeff_warn(a:int):returna+1@typing_custom(convertors={int:to_array,"b":to_array_2},exclude=["c",pd.DataFrame])deff_custom(a:np.ndarray,b:np.ndarray,c:np.ndarray,d:np.ndarray):returna+1,b+1,c+1,d+1f_raise(1)# Returns 2, as expected# noinspection PyTypeCheckerf_raise(1.5)# Raises TypeError# noinspection PyTypeCheckerf_convert(1.5)# Returns 2 (converted 1.5 into 1)# noinspection PyTypeCheckerf_convert("foo")# Raises ValueError (while trying to convert 'foo' to interger)# noinspection PyTypeCheckerf_warn(1.5)# Returns 2.5, and warns# noinspection PyTypeCheckera_,b_,c_,d_=f_custom(1,2,3,pd.DataFrame([4]))# a_ is np.array([2, 3])# b_ is np.array([3, 7])# c_ is 4# d_ is pd.DataFrame([5])
Package: adua
AduaAdua is a Python package that provides various functionalities, including web search, face recognition, audio processing, and more. It is designed to assist users with tasks and interactions using speech and text.FeaturesWeb search: Perform searches on popular search engines.Face recognition: Capture faces from video and perform facial recognition.Audio processing: Convert text to speech and speech to text.Wolfram Alpha integration: Get answers to queries using Wolfram Alpha.Image generation: Generate images based on queries.OpenAI GPT integration: Get answers to questions using OpenAI GPT.InstallationYou can install Adua using pip:pipinstalladua

Usage
Here's a quick example of how to use Adua in your Python code:<code>from adua import Adua# Initialize Aduaadua = Adua()# Perform a web searchadua.web_search('howtocreatePythonpackage')# Capture faces from videofaces, face_num, face_locations, face_encodings, frame = adua.Capture_face_vid()# Convert text to speechadua.speak('Hello,howareyou?')# Convert speech to textquery=adua.listen()</code>
License
ThisprojectislicensedundertheMITLicense.
Package: adul
AdulAdul package python for Super AI Engineerhttps://pypi.org/project/adul/Installpip3 install -r requirements.txt
pip3 install adulUsagefrom adul import super_ai
cd demohome work 1นับว่า alphabet ใน name list กี่ตัวpython3 count_alphabet.pyhome work 2เขียนโปรแกรมหาค่าสูงสุดในลิสต์python3 maximum_list.pyเขียน 8 puzzlepython3 eight_puzzle.pyเขียน depth-first search พร้อม อะนิเมชั่นpython3 depth_first_search.pyเลือกข้อมูล 1 อย่างจาก Kaggle มาทำการ visualize ข้อมูลในรูปแบบกราฟต่างๆpython3 visualize_kaggle.py
Package: adult-dataset
adult-datasetA PyTorch dataset wrapper for theAdult (Census Income)dataset.
Adult is a popular dataset in machine learning fairness research.This package provides theadult.Adultclass:
atorch.utils.data.Datasetsloading and, optionally, downloading the
Adult dataset.
It can be used like theMNISTdataset intorchvision.Beyondadult.Adult, this package also providesadult.AdultRaw,
which works just asadult.Adult, but
does not standardize the features in the dataset and does not apply one-hot encoding.Installationpipinstalladult-datasetBasic UsagefromadultimportAdult# load (if necessary, download) the Adult training datasettrain_set=Adult(root="datasets",download=True)# load the test settest_set=Adult(root="datasets",train=False,download=True)inputs,target=train_set[0]# retrieve the first sample of the training set# iterate over the training setforinputs,targetiniter(train_set):...# Do something with a single sample# use a PyTorch data loaderfromtorch.utils.dataimportDataLoaderloader=DataLoader(test_set,batch_size=32,shuffle=True)forepochinrange(100):forinputs,targetsiniter(loader):...# Do something with a batch of samplesAdvanced UsageTurn off status messages while downloading the dataset:Adult(root=...,output_fn=None)Use theloggingmodule for logging status messages while downloading the
dataset instead of placing the status messages onsys.stdout.importloggingAdult(root=...,output_fn=logging.info)
Package: advai-versus
AdvaiThis is a Python package that provides a basic FastAPI application, demonstrating a simple RESTful API structure.It's current version is intended test for deploying packages to pypiFeaturesThis is a namespace test applicationInstallationYou can install advai directly from PyPI:pip install advaiQuick StartAfter installation, you can start using advai by creating a FastAPI application. Here's a quick example:from advai import create_app

app = create_app()

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)To run the app, use the following command:uvicorn your_script_name:app --reloadRunning TestsTo run tests, navigate to your project directory and execute:pytestDocker and Kubernetes SupportThis template does not currently contain docker or kubenetes configurationsContributingContributions are welcome! Please read our contributing guidelines for more information.Licenseadvai is released under the MIT License.AuthorsAdvai Development Lead