Saravanan666/pypi-org huggingface dataset:
Package: ctpysql
cautious-pysqlCTpysql helps you to code mysql queries as fast as possible ðŸš„ðŸ”¥UsageInstallationUsepip install ctpysqlto install thectpysqland it's dependencies on your pc (or venv).import module with:from ctpysql import ctpysqlto use it in your project.How it works?You may import mysql.connector to work with ctpysql, just use:from mysql.connector import (connection, Error)and make a connection like this:cnx = connection.MySQLConnection(user='root', password='password', host='127.0.0.1', database='db')then make an object fromctpysqlclass using this connection:obj = ctpysql(cnx)after these steps you are able to use this library correctly, for example, insert query using dictionary values:obj.insert('users', {'username': 'user1', 'password': 'password1'})Supported queriesSupported queries at this version (they will be more soon):InsertLast rowid inserted returninsert('table', {dictionary values})Truncate:truncate('table')SelectSelected row(s) returnfetch('table', 'case [all or one]', 'type [dic or None]', **data)Example:fetch('users', 'one', None, id=3)Select allSelected row(s) returnfetchall('table', 'type [dic or None]')Example 1:fetchall('products', None)Example 2:fetchall('products', 'dic')UpdateAffected rows count returnupdate('table', 'condition column name', 'condition column value', **data)Example:update('users', 'username', usernameVar, password=123, email=a@a.com)DeleteDeleted rows count returndelete('table', 'condition column name', 'condition value')Drop"Success" or error returndrop('case', 'name')Example:drop('table', 'product')Example2:drop('view', '[mysql queries]')OrderFetched items returnorder('table', **data(column1="order like ASC", column2="order like DESC"))Example:order('products', name="ASC", id="DESC")Create table"Success" or error returncreate_table('table name', **data)Example:create_table('users', id="INT AUTO_INCREMENT PRIMARY KEY", name="VARCHAR(255)")BetweenFetched rows returnbetween('table name', 'type [dic or None]', 'condition column', 'first parameter', 'second parameter')Example:between('products', None, 'id', 1, 4)Don't forget!Don't forget this is a class and you should call this functions from an object of class. A full example here:frommysql.connectorimport(connection,Error)fromctpysqlimportctpysqlconnection=connection.MySQLConnection(user='root',password='password',host='127.0.0.1',database='testdb')object=ctpysql(cnx)object.insert('products',{'name':'Laptop','brand':'Asus'})
Package: ctr
Concatenate files w/ headers.Free software: MIT licenseInstallationpip install ctrYou can also install the in-development version with:pip install https://github.com/samesense/python-ctr/archive/master.zipDocumentationhttps://python-ctr.readthedocs.io/DevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.0.0 (2020-01-03)First release on PyPI.
Package: ctr-acn
DeepCTRDeepCTR is aEasy-to-use,ModularandExtendiblepackage of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models.You can use any complex model withmodel.fit()，andmodel.predict().Providetf.keras.Modellike interface forquick experiment.exampleProvidetensorflow estimatorinterface forlarge scale dataanddistributed training.exampleIt is compatible with bothtf 1.xandtf 2.x.Let'sGet Started!(Chinese Introduction) andwelcome to join us!Models ListModelPaperConvolutional Click Prediction Model[CIKM 2015]A Convolutional Click Prediction ModelFactorization-supported Neural Network[ECIR 2016]Deep Learning over Multi-field Categorical Data: A Case Study on User Response PredictionProduct-based Neural Network[ICDM 2016]Product-based neural networks for user response predictionWide & Deep[DLRS 2016]Wide & Deep Learning for Recommender SystemsDeepFM[IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR PredictionPiece-wise Linear Model[arxiv 2017]Learning Piece-wise Linear Models from Large Scale Data for Ad Click PredictionDeep & Cross Network[ADKDD 2017]Deep & Cross Network for Ad Click PredictionsAttentional Factorization Machine[IJCAI 2017]Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention NetworksNeural Factorization Machine[SIGIR 2017]Neural Factorization Machines for Sparse Predictive AnalyticsxDeepFM[KDD 2018]xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender SystemsAutoInt[arxiv 2018]AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural NetworksDeep Interest Network[KDD 2018]Deep Interest Network for Click-Through Rate PredictionDeep Interest Evolution Network[AAAI 2019]Deep Interest Evolution Network for Click-Through Rate PredictionFwFM[WWW 2018]Field-weighted Factorization Machines for Click-Through Rate Prediction in Display AdvertisingONN[arxiv 2019]Operation-aware Neural Networks for User Response PredictionFGCNN[WWW 2019]Feature Generation by Convolutional Neural Network for Click-Through Rate PredictionDeep Session Interest Network[IJCAI 2019]Deep Session Interest Network for Click-Through Rate PredictionFiBiNET[RecSys 2019]FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate PredictionFLEN[arxiv 2019]FLEN: Leveraging Field for Scalable CTR PredictionCitationWeichen Shen. (2018). DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models.https://github.com/shenweichen/deepctr.If you find this code useful in your research, please cite it using the following BibTeX:@misc{shen2018deepctr,author={Weichen Shen},title={DeepCTR: Easy-to-use,Modular and Extendible package of deep-learning based CTR models},year={2018},publisher={GitHub},journal={GitHub Repository},howpublished={\url{https://github.com/shenweichen/deepctr}},}DisscussionGroup 交流群Please follow our wechat to join group:公众号：浅梦的学习笔记wechat ID:deepctrbot
Package: ctrader-fix
cTraderFixPyA Python package for interacting with cTrader FIX API.This package uses Twisted and it works asynchronously.Free software: MITDocumentation:https://spotware.github.io/cTraderFixPy/.FeaturesWorks asynchronously by using TwistedAllows you to easily interact with cTrader FIX API and it manages everything in backgroundGenerate FIX message by using Python objectsInsallationpip install ctrader-fixConfigConfig file sample:{"Host":"","Port":0,"SSL":false,"Username":"","Password":"","BeginString":"FIX.4.4","SenderCompID":"","SenderSubID":"QUOTE","TargetCompID":"cServer","TargetSubID":"QUOTE","HeartBeat":"30"}Usagefromtwisted.internetimportreactorimportjsonfromctrader_fiximport*# Callback for receiving all messagesdefonMessageReceived(client,responseMessage):print("Received: ",responseMessage.getMessage().replace("","|"))messageType=responseMessage.getFieldValue(35)ifmessageType=="A":print("We are logged in")# Callback for client disconnectiondefdisconnected(client,reason):print("Disconnected, reason: ",reason)# Callback for client connectiondefconnected(client):print("Connected")logonRequest=LogonRequest(config)send(logonRequest)# you can use two separate config files for QUOTE and TRADEwithopen("config-trade.json")asconfigFile:config=json.load(configFile)client=Client(config["Host"],config["Port"],ssl=config["SSL"])# Setting client callbacksclient.setConnectedCallback(connected)client.setDisconnectedCallback(disconnected)client.setMessageReceivedCallback(onMessageReceived)# Starting the client serviceclient.startService()reactor.run()Please check documentation or samples for a complete example.DependenciesTwisted
Package: ctrader-open-api
OpenApiPyA Python package for interacting with cTrader Open API.This package uses Twisted and it works asynchronously.Free software: MITDocumentation:https://spotware.github.io/OpenApiPy/.FeaturesWorks asynchronously by using TwistedMethods return Twisted deferredsIt contains the Open API messages files so you don't have to do the compilationMakes handling request responses easy by using Twisted deferredsInsallationpip install ctrader-open-apiUsagefromctrader_open_apiimportClient,Protobuf,TcpProtocol,Auth,EndPointsfromctrader_open_api.messages.OpenApiCommonMessages_pb2import*fromctrader_open_api.messages.OpenApiCommonMessages_pb2import*fromctrader_open_api.messages.OpenApiMessages_pb2import*fromctrader_open_api.messages.OpenApiModelMessages_pb2import*fromtwisted.internetimportreactorhostType=input("Host (Live/Demo): ")host=EndPoints.PROTOBUF_LIVE_HOSTifhostType.lower()=="live"elseEndPoints.PROTOBUF_DEMO_HOSTclient=Client(host,EndPoints.PROTOBUF_PORT,TcpProtocol)defonError(failure):# Call back for errorsprint("Message Error: ",failure)defconnected(client):# Callback for client connectionprint("\nConnected")# Now we send a ProtoOAApplicationAuthReqrequest=ProtoOAApplicationAuthReq()request.clientId="Your application Client ID"request.clientSecret="Your application Client secret"# Client send method returns a Twisted deferreddeferred=client.send(request)# You can use the returned Twisted deferred to attach callbacks# for getting message response or error backs for getting error if something went wrong# deferred.addCallbacks(onProtoOAApplicationAuthRes, onError)deferred.addErrback(onError)defdisconnected(client,reason):# Callback for client disconnectionprint("\nDisconnected: ",reason)defonMessageReceived(client,message):# Callback for receiving all messagesprint("Message received:\n",Protobuf.extract(message))# Setting optional client callbacksclient.setConnectedCallback(connected)client.setDisconnectedCallback(disconnected)client.setMessageReceivedCallback(onMessageReceived)# Starting the client serviceclient.startService()# Run Twisted reactorreactor.run()Please check documentation or samples for a complete example.DependenciesTwistedProtobuf
Package: ctransformer-core
ctransformersFollow up the stopping updated project ctransformers; continue working on it.install it by (pip/pip3):pip install ctransformers-0.2.28.tar.gzrun it by (python/python3):python -m ctransformersUser interfaces are available to choose (eithr CLI or GUI); while chosen, GGUF file(s) in the current directory will be searched and detected.This is another solo connector core; same as llama-core; being able to work independently as well.referencectransformersAll other functions are same as ctransformers; please see the reference above.build from ctransformers-(version).tar.gz (examples below are for CPU)According to the latest note inside vs code, msys64 is recommended by Microsoft; or you can opt w64devkit or etc. as source of your gcc and g++ compilers.for windows user(s):$env:CMAKE_GENERATOR = "MinGW Makefiles"
$env:CMAKE_ARGS = "-DCMAKE_C_COMPILER=C:/msys64/mingw64/bin/gcc.exe -DCMAKE_CXX_COMPILER=C:/msys64/mingw64/bin/g++.exe"
pip install ctransformers-(version).tar.gzIn mac, xcode command line tools are recommended by Apple for dealing all coding related issue(s); or you can bypass it for your own good/preference.for mac user(s):pip3 install ctransformers-(version).tar.gzMake sure your gcc and g++ are >=11; you can check it by: gcc --version and g++ --version if you opt to build the wheel from source code.
Package: ctranslitcodec
This package contains codecs for transliterating ISO 10646 texts
into best-effort representations using smaller coded character sets
(ASCII, ISO 8859, etc.). It is a C reimplementation of the
[translitcodec][1] module by Jason Kirtland, as well as a
drop-in replacement.The translation tables used by the codecs are from the [transtab][2]
collection by [Prof. Markus Kuhn][3], with some extensions by
[Fazal Majid][4].Three types of transliterating codecs are provided:“long”, using as many characters as needed to make a naturalreplacement.  For example, u00e4 LATIN SMALL LETTER A WITH
DIAERESISäwill be replaced withae.“short”, using the minimum number of characters to make a
replacement.  For example, u00e4 LATIN SMALL LETTER A WITH
DIAERESISäwill be replaced witha.“one”, only performing single character replacements.  Characters
that can not be transliterated with a single character are passed
through unchanged. For example, u2639 WHITE FROWNING FACE☹will be passed through unchanged.Using the codecs is simple:>>> import ctranslitcodec
>>> import codecs
>>> codecs.encode(u'fácil € ☺', 'ctranslit/long')
u'facil EUR :-)'
>>> codecs.encode(u'fácil € ☺', 'ctranslit/short')
u'facil E :-)'[1]:https://pypi.org/project/translitcodec/[2]:https://www.cl.cam.ac.uk/~mgk25/unicode.html#libs[3]:https://www.cl.cam.ac.uk/~mgk25/[4]:https://majid.info/
Package: ctrate
# CTrate
Version 0.1.beta![CTrate_white_bg](https://user-images.githubusercontent.com/60097047/164969088-94b6cf0c-4027-4f15-bab7-4c66d9968fc1.png)AboutRate calculation for models and molecular dynamics trajectories based on an assorted of rate theories.List of Theories :Fermi Golden RuleMarcus TheoryTransient Localization TheorySecond Order CumulantUsage:In NYUSH HPC
module use /xspace/sungroup/modules/module load ctratethen you can simply callctrate <input file> <options>For development:Set the following command to your ~/.bashrc: (assuming The CTRate is in home directory, ~/)module load gcc/7.3 cuda/11.2export CTRATE=~/CTrateexport PATH=$PATH:$CTRATE/binCopyright, Sun Group, NYUSH, 2022.
Package: ctrfoobar2000
Description----------------------Control Foobar2000 with python + Command lineUsage-----------Usage: foobar.py [options]usage: foobar.py http [-h] [-p] [-t PLAY_TRACK] [-s] [-P] [-n] [-r] [-R][-V VOLUME] [-m] [-i] [-f ADDFOLDER] [-F ADDFOLDERPLAY][-c] [-d [DEL_TRACK [DEL_TRACK ...]]] [-l] [-b][-S TYPE_CONTROLLER] [-x] [-H HOST] [-O PORT] [-?] [-g][-T SECTION] [-E OPTION OPTION] [-a DIR_ALIAS][-L LEVEL_ALIAS]optional arguments:-h, --help            show this help message and exit-p, --play            Play Playback-t PLAY_TRACK, --play-track PLAY_TRACKPlay Playback Track No-s, --stop            Stop Playback-P, --pause           Pause Playback-n, --next            Next Play-r, --previous        Previous Play-R, --random          Play Random-V VOLUME, --volume VOLUMESet Volume, range is -100 <= value <= 0-m, --mute            Mute Volume-i, --info            Get info current Playing-f ADDFOLDER, --addfolder ADDFOLDERAdd Remote Folder Queue [HTTP]-F ADDFOLDERPLAY, --addfolderplay ADDFOLDERPLAYAdd Remote Folder Queue & Play it [HTTP]-c, --clear-playlist  Clear Current Playlist [HTTP]-d [DEL_TRACK [DEL_TRACK ...]], --del-track [DEL_TRACK [DEL_TRACK ...]]Delete Playlist [HTTP], example: foobar -d 1 2 3-l, --list            List Playlist-b, --browser         Browser Library-S TYPE_CONTROLLER, --type-controller TYPE_CONTROLLERSet Type Of Controller [com,http]-x, --store-config    Store Set Type Of Controller [com,http]-H HOST, --host HOST  Remote Host control Address [HTTP]-O PORT, --port PORT  Remote Port control Address [HTTP]-?, --usage           Print All Help-g, --read-config     Read config file-T SECTION, --section SECTIONSet Section Config-E OPTION OPTION, --option OPTION OPTIONSet Option Config-a DIR_ALIAS, --dir-alias DIR_ALIASRoot of Directory Alias On Server-L LEVEL_ALIAS, --level-alias LEVEL_ALIASDependency------------------+ Foobar2000 COMServer2Helper+ Foobar2000 HTTP ControlAuthor-----------licface (licface@yahoo.com)
Package: ctrl4ai
Ctrl4AIThis is a helper package for Machine Learning and Deep Learning solutions.For documentation, please readHELP.mdFor demo on usage, please checkREADME.ipynbLastest Version: 1.0.24HighlightsOpen Source Package with emphasis on data preprocessing so far.Self intelligent methods that can be employed at the levels of abstraction or customization.The flow of auto-preprocessing is orchestrated compatible to the learning type.Parameter tuning allows users to transform the data precisely to their specifications.Developed computations for inspecting the data to discover its type, distribution, correlation etc. which are handled in the background.DependenciesCtrl4AI requires:Python 3 (tested under Python 3.8)InstallationThe easiest way to install the latest release version of Ctrl4AI is viapip:pipinstallctrl4aiIn case you getERROR: Could not install packages due to an EnvironmentError, try usingpipinstallctrl4ai--userCheck for the latest available version inCtrl4AIImportImport any module from the package through the following method:fromctrl4aiimportprepdatafromctrl4aiimportautomlLearn to useUnderstand what each function does by usinghelp():help(automl.preprocess)ChangeLogAdded features to transform ordinal values (from string to integers)ToDoModel specific pre-processingPrepare dataset for bagging AlgorithmsPreprocessing for Text,Image,Audio,Video AnalyticsDepreciation NoticeAll functions inctrl4ai.preprocessingare depreciated by the end of 2022 based on the prior notice. Please plan to switch to the same functions inctrl4ai.prepdataif you're still using it.About UsProprietary Code ofVKreaTProject Owner:Shaji JamesCollaborators:Charu,SelvaVisitCtrl4AI.comfor more updates
Package: ctrl4bi
Ctrl4BIThis is a helper package for Business Intelligence solutions.For documentation, please readHELP.mdFor demo on usage, please checkREADME.ipynbLastest Version: 1.0.5HighlightsConnector for AWS S3 - Bulk Download Files/Directories in S3 BucketPII/PHI scrubbing - Removes occurrences of SSN / Driver License / Account Numbers etc from textsETL testing - Column Level CheckParse JSON filesDependenciesCtrl4BI requires:Python 3 (tested under Python 3.8)InstallationThe easiest way to install the latest release version of Ctrl4BI is viapip:pipinstallctrl4biIn case you getERROR: Could not install packages due to an EnvironmentError, try usingpipinstallctrl4bi--userCheck for the latest available version inCtrl4BIImportImport any module from the package thru the following method:fromctrl4biimportcleanserfromctrl4biimportetl_testingLearn to useUnderstand what each functions does by usinghelp():help(cleanser.scrub_pii)ChangeLogSort and Compare two datasets (When joining is not possible)Flatten JSON & DictionaryConnector for AWS S3ToDoConnectors for various data sourcesRelationalize Unstructured DataAbout UsProprietary Code ofVKreaTProject Owner:Shaji James
Package: ctrl-bench
Continual Transfer Learning BenchmarkThe CTrL benchmark aims to help research by studying the transfer behaviour of different models in the Lifelong Learning context.For a quick presentation of the benchmark, please check outthis blog post.For more details, the paper is available onarxiv.Creating a Stream:TheTaskGeneratorclass is at the center of the implementation of the CTrL Benchmark.
It gives access to a high-level API allowing to seamlessly generate a wide variety of streams with a loose coupling between the different components such as the underlying dataset(s), the strategy to generate the tasks (split, incremental, mixture of datasets, ...) and the processing to apply to each task.The 3 main components of aTask Generatorare:A pool of concepts to select from to generate the tasks. It can be a few classes, a full dataset or even a mixture of datasets.A pool of transformation that can be modified or combined to apply specific processing to the data for each task.A Strategy, describing how to combine the conecpts and trasnformation over time to generate an actual stream.Each of these components can be created by hand or using our automaticTaskGeneratorcreation tool using yaml configuration files.For examples simply executingimportctrltask_gen=CTrl.get_stream('s_minus')will return the corresponding task generator that be used either directy to generate tasks on the fly:t1=task_gen.add_task()t2=task_gen.add_task()t3=task_gen.add_task()...or as an iterator:fortintask_gen():...Available streams:In the current version, only the streams of the CTrL benchmark are directly available, they can be obtained by passing the followingnamearguments inctrl.get_stream:S+:"s_plus"S-:"s_minus"Sin:"s_in"Sout:"s_out"Spl:"s_pl"Slong:"s_long"More documentation and details on the internal components will be progressively added.See the CONTRIBUTING file for how to help out.LICENSESee the LICENSE file.
Package: ctrl-benchmark
Continual Transfer Learning BenchmarkThe CTrL benchmark aims to help research by studying the transfer behaviour of different models in the Lifelong Learning context.For a quick presentation of the benchmark, please check outthis blog post.For more details, the paper is available onarxiv.Creating a Stream:TheTaskGeneratorclass is at the center of the implementation of the CTrL Benchmark.
It gives access to a high-level API allowing to seamlessly generate a wide variety of streams with a loose coupling between the different components such as the underlying dataset(s), the strategy to generate the tasks (split, incremental, mixture of datasets, ...) and the processing to apply to each task.The 3 main components of aTask Generatorare:A pool of concepts to select from to generate the tasks. It can be a few classes, a full dataset or even a mixture of datasets.A pool of transformation that can be modified or combined to apply specific processing to the data for each task.A Strategy, describing how to combine the conecpts and trasnformation over time to generate an actual stream.Each of these components can be created by hand or using our automaticTaskGeneratorcreation tool using yaml configuration files.For examples simply executingimportctrltask_gen=ctrl.get_stream('s_minus')will return the corresponding task generator that be used either directy to generate tasks on the fly:t1=task_gen.add_task()t2=task_gen.add_task()t3=task_gen.add_task()...or as an iterator:fortintask_gen:print(t)Available streams:In the current version, only the streams of the CTrL benchmark are directly available, they can be obtained by passing the followingnamearguments inctrl.get_stream:S+:"s_plus"S-:"s_minus"Sin:"s_in"Sout:"s_out"Spl:"s_pl"Slong:"s_long"More documentation and details on the internal components will be progressively added.See the CONTRIBUTING file for how to help out.LICENSESee the LICENSE file.
Package: ctrlchandler
Like atexit but for ctrl+cTested against Windows 10 / Python 3.10 / Anacondapip install ctrlchandler# atexitimportatexitdefprintx(bu=12,ba=3223):print(bu,ba)print('ciao')atexit.register(printx,bu='nice',ba='ba')exit()nicebaciaoProcessfinishedwithexitcode0fromctrlchandlerimportset_console_ctrl_handlerdefprintx(bu=12,ba=3223):print(bu,ba)print('ciao')set_console_ctrl_handler(returncode=1,func=printx,bu=12011,ba=32231111)whileTrue:passAfterpressingctrl+c^C1201132231111ciaoProcessfinishedwithexitcode1
Package: ctrldaemon
Service command wrapper for Python.UsageCreate object`python from ctrldaemon import ControlDaemon obj =ControlDaemon("service")# Example "httpd" (Apache) `Start service`python from ctrldaemon import ControlDaemon obj =ControlDaemon("service")# Example "httpd" (Apache) obj.start() `Stop service`python from ctrldaemon import ControlDaemon obj =ControlDaemon("service")# Example "httpd" (Apache) obj.stop() `Restart service`python from ctrldaemon import ControlDaemon obj =ControlDaemon("service")# Example "httpd" (Apache) obj.restart() `Memory`python from ctrldaemon import ControlDaemon obj =ControlDaemon("service")# Example "httpd" (Apache) obj.get_memory_usage() `
Package: ctrl.engine
A general purpose robotics engineInstallationPython:pip install ctrl.engineThis library is tested with Ubuntu 19.04 and Python 3.7.5. It was developed specifically for use with robotics using a Raspberry Pi.Modulesfromctrlengine.controllersimportPIDfromctrlengine.filtersimportsimple_moving_averagefromctrlengine.filtersimportexponentially_weighted_moving_averagefromctrlengine.sensorsimportcamerafromctrlengine.interfaceimportxbox_ctrlfromctrlengine.utilimportloggerfromctrlengine.aiimportface_detectionfromctrlengine.aiimportimage_classificatoinfromctrlengine.aiimportobject_detectionfromctrlengine.aiimportpose_detectionfromctrlengine.ai.cloud_visionimportcloud_visionfromctrlengine.ai.text_to_speechimporttext_to_speechfromctrlengine.ai.azure_visionimportazure_vision