Saravanan666/pypi-org huggingface dataset:
Package: intel-extension-for-tensorflow-lib
Intel® Extension for Tensorflow* libraryIntel® Extension for Tensorflow* library is the support library for Intel® Extension for Tensorflow*(https://pypi.org/project/intel-extension-for-tensorflow/). While Intel® Extension for Tensorflow* itself is a pure Python package, Intel® Extension for Tensorflow* library contains the binary (C/C++) parts of the library, including Python bindings, Intel XPU(GPU, CPU, etc) devices support.Documentation:Intel® Extension for TensorFlow* online document website.SecuritySee Intel'sSecurity Centerfor information on how to report a potential security issue or vulnerability.See also:Security Policy
Package: intelhex
IntroductionThe Intel HEX file format is widely used in microprocessors and microcontrollers
area (embedded systems etc) as the de facto standard
for representation of code to be programmed into microelectronic devices.This work implements anintelhexPython library to read, write,
create from scratch and manipulate data from Intel HEX file format.The distribution package also includes several convenience Python scripts,
including “classic”hex2binandbin2hexconverters and more,
those based on the library itself. Check the docs to know more.LicenseThe code is distributed under BSD license,
seeLICENSE.txt.In short: you can use IntelHex library in your project withoutanyrestrictions.Supported Python versionsIntelHex library supports Python 3 (3.5 or later) only. The 2.2.1 release was
the last one which has been checked against Python 2.7 and Python 3 until 3.5.InstallInstall usingpip(recommended, no separate download required):pip install intelhexDownloadhttps://pypi.org/project/IntelHex/https://github.com/python-intelhex/intelhex/releasesSource code, bug reports, patchesIntelHex on GitHub:https://github.com/python-intelhex/intelhexUser manualUser manual for IntelHex is available in the sourcesdocs/manual/directory.
You can browse User Manual online:https://readthedocs.org/projects/python-intelhex/ChangelogSeeNEWS.rst
Package: intelinair-utils
intelinair-utilsSet of common code to be used across a number of intelinair reposUnit Test Coverage:Integration Test CoverageRepository StructurescriptsLocation for bash utility scriptssrcLocation for package source codetestsLocation for unit and integration tests for packageUsageDevelopmentCreate a virtual environment and install the dev requirementscondacreate-nintelinair-utilspython=3.8
condaactivateintelinair-utils
pipinstall-rrequirements_dev.txtTestingTo run the unit and integration tests: (requires a valid ~/.agmri.cfg)./scripts/test.shView coverage html report at ./html/{unit|integration}_coverage/index.htmlGenerate DocumentationAll the docstrings can be rendered into html for easy viewing./scripts/generate_docs.shView docs at ./html/docs/intelinair_utils/index.htmlReleasingTo release a new version run the following in the master branch being sure to specify the type of release../scripts/do_release.sh{patch|minor|major}InstallingTo install this package run one of the following# Install a specific versionpipinstallgit+https://github.com/intelinair/intelinair-utils@v0.0.1# Install the latest versionpipinstallgit+https://github.com/intelinair/intelinair-utilsPackage UsageRequirementsSet up aws credentials. If you don't have credentials ask David Wilsonpipinstallawscli
awsconfigureSet up agmri credentials (seeintelinair_utils.agmri_apifor more details)echo'[prod]admin_username = <YOUR_USERNAME>admin_password = <YOUR_PASSWORD>'>~/.agmri.cfgInstallationInstall the latest versionpipinstallgit+https://github.com/intelinair/intelinair-utilsUsageStart a python shellfromintelinair_utilsimportAgmriApiapi=AgmriApi('prod')print(api.get('flights/'+'M933ZLYGP'))
Package: intelino-trainlib
intelino-trainlib-pyPython library (SDK) for interacting with the intelino smart train.OverviewIntelino Smart Train is an award-winning programmable robotic toy that is both fun and educational. Powered by innovative robotic tech, the smart train offers multiple programming modes suitable for users of different ages.Learning is more meaningful and relatable when experimenting with and simulating real world problems. Younger kids use screen-free activities and tactile coding to operate the smart trains and make them run on schedule. And older users, students and makers use our advanced tools to build smart rail systems and experiment with autonomous driving, collision avoidance, route optimization, resource sharing and much more!We offer both synchronous and asynchronous Python programming libraries for the intelino smart train. Theintelino-trainlibis our synchronous Python library. It gives access to our full-featured API, enables event-based programming (through threads) and allows to interactively control one or multiple smart trains. This library is well suited for students and users that are new to Python or text-based programming, in general. And programmers with more advanced skills may prefer our asynchronous libraryintelino-trainlib-asyncwhich offers an extended list of API features, Rx-based reactive programming and superior performance.InstallationThe intelino trainlib is available on PyPi and can be installed with pip:python3 -m pip install intelino-trainlibScanningpython3 -m intelino.scanLocal development:git clone git://github.com/intelino-code/intelino-trainlib-py
cd intelino-trainlib-py

python3 -m venv .env
source .env/bin/activate
python3 -m pip install -r requirements.txt
python3 -m pip install -r requirements-dev.txt
python3 -m pip install -e .
Package: intelino-trainlib-async
intelino-trainlib-async-pyAsynchronous Python library (SDK) for interacting with the intelino smart train.OverviewIntelino Smart Train is an award-winning programmable robotic toy that is both fun and educational. Powered by innovative robotic tech, the smart train offers multiple programming modes suitable for users of different ages.Learning is more meaningful and relatable when experimenting with and simulating real world problems. Younger kids use screen-free activities and tactile coding to operate the smart trains and make them run on schedule. And older users, students and makers use our advanced tools to build smart rail systems and experiment with autonomous driving, collision avoidance, route optimization, resource sharing and much more!We offer both synchronous and asynchronous Python programming libraries for the intelino smart train. Theintelino-trainlibis our synchronous Python library. It gives access to our full-featured API, enables event-based programming (through threads) and allows to interactively control one or multiple smart trains. This library is well suited for students and users that are new to Python or text-based programming, in general. And programmers with more advanced skills may prefer our asynchronous libraryintelino-trainlib-asyncwhich offers an extended list of API features, Rx-based reactive programming and superior performance.InstallationThe asynchronous intelino trainlib is available on PyPi and can be installed with pip:python3 -m pip install intelino-trainlib-asyncScanningpython3 -m intelino.scanLocal development:git clone git://github.com/intelino-code/intelino-trainlib-async-py
cd intelino-trainlib-async-py

python3 -m venv .env
source .env/bin/activate
python3 -m pip install -r requirements.txt
python3 -m pip install -r requirements-dev.txt
python3 -m pip install -e .
Package: intelix
Sophos Labs IntelixClient for the Sophos Labs Intelix APIhttps://api.labs.sophos.com/doc/index.htmlexampleConnect to the API by providing your client ID and secret keyimport intelix
clientId = "xxxxxxxxxxx"
secret = "xxxxxxxxxxxxxxxxxxxxxxxxx"
url = "www.sophos.com"

i = intelix.client(clientId,secret)
i.url_lookup(url)
print(i.productivityCategory)
Package: intel-jtag-uart
intel_jtag_uartis a Python module that can be used to interact with the JTAG UART instances inside
Intel FPGA designs.The official ways to do this are either interactively, throughnios2-terminalutility,
or by scripting some TCL code inside the Quartus System Console. There is no official
straightforward way to interact with a JTAG UART with Python.This module is a wrapper around thejtag_atlanticshared library/DLL that is part of any
modern Quartus installation. (jtag_atlanticis the internal code name for the JTAG UART.)To use this module, you need access to thejtag_atlanticshared library as well as thejtag_clientshared library, which provides lower level JTAG-related functions.UsageInstall this package withpip3 install intel_jtag_uart.Point to thejtag_atlanticandjtag_clientshared libraries, so that the module can find it.The module uses the following sequence to find these libraries:Look in the directory in whichintel_jtag_uart.pyis locatedLook in the directory in which the executable that uses this module is locatedUse the OS-provided way to find shared libraries. (E.g. for Linux, it will check
the directories of the$LD_LIBRARY_PATHenvironment variable.)Look in the$QUARTUS_ROOTDIRdirectoryMost default Quartus installations will have$QUARTUS_ROOTDIRenvironment variable
set to the correct directory, so everything should just work™.Use some variation of the script below:importintel_jtag_uarttry:ju=intel_jtag_uart.intel_jtag_uart()exceptExceptionase:print(e)sys.exit(0)ju.write(b'r')time.sleep(1)print("read: ",ju.read())The script sendsrto the JTAG UART, waits 1 seconds for a reply, and reads the
reply, if there is any. If you have an Arrow/Terasic DECA FPGA board, you can check
things out right away with aprecompiled example bitstream.Full List of Functions/MethodsUsethe source, Luke!This module is a very thin wrapper around a handful of function calls that are mostly self-explanatory.Bug Reports/Comments/QuestionsBug reports, comments, or questions can be entered throughthe GitHub issue trackerof this project.ReferencesThe Intel JTAG UART - Add a Serial Console to Your Design without Extra IO PinsWrite Your Own C and Python Clients for the Intel JTAG UARTjtag_uart_exampleprojectintel-jtag-uart on pypi.org
Package: intellifire4py
Intellifire4PyFeaturesTODORequirementsTODOInstallationYou can installIntellifire4PyviapipfromPyPI:$pipinstallintellifire4pyUsagePlease see the [API Reference] for details.ContributingContributions are very welcome.
To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,Intellifire4Pyis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was generated from@cjolowicz'sHypermodern Python Cookiecuttertemplate.
Package: intelli-gateway
Simple StuffUsageimport librarycreate library instance using your email and passwordauthenticate clientmake requestsUnauthenticated requests will fail. Authentication does not need to be done everytime
when one wants to make a request, it can be done once and works across all requests.Sample Codefromintelli_gateway.gateway_clientimportGatewayClientclient=GatewayClient("email","password")# Authenticate Clientclient.authenticate()# Get Account Informationclient.account_information()# Send single smsclient.send_sms(message="Hi. How are you?",receiver="263XXXXXXXXX",sender_id="Tumai")# Send bulk smsclient.send_bulk_sms(message="Hi. How are you?",receivers=["263XXXXXXXXX","263XXXXXXXXX"],sender_id="Tumai")# Send Emailclient.send_email(body="Hi. How are you?",setting_id="UUID",receiver_email="email",subject="Greetings")# Send Bulk Emailsclient.send_bulk_email(body="Hi. Hello everyone. We are testing messaging!",setting_id="UUID",recipients=["me@gmail.com","i@gmail.com"],subject="Testing Tumai")Process Flowflowchart TD
    A[Use IntelliAfrica Solutions Gateway Python Client] --> B[Create Instance];
    B --> C[Authenticate Client];
    C --> D{Authenticated?};
    D -- Yes --> E[Process Request];
    D -- No --> F[Raise Exception]
Package: intelligentML
Iltelligent Machine Learning Task using PythonAn intelligent machine learning package. A Python library for handling duplicate data and performing data preprocessing on datasets. This library provides functions to identify and remove duplicate rows, handle missing values, and prepare data for analysis. All you have to do just provide the dataset url or path the library automatically do those tasks.Free software: MIT licenseDocumentation:https://Nasif-Azam.github.io/intelligentMLFunctionality of the Packagehd_and_dp(dataPath) : Both handle duplication and data preprocessing by sending the dataset url or path link.handle_duplication(dataPath) : Handle duplication by sending the dataset url or path link.dataPreprocessing(dataPath) : Data preprocessing by sending the dataset url or path link.UsageMake sure you have Python installed in your system.Run Following command in the CMD.pip install intelligentMLExample-1# test.py:

from intelligentML import hd_and_dp
url = "example.csv"
finalDataset = hd_and_dp(url)
print(f"Final Preprocessed Dataset: \n{finalDataset}")Example-2# test.py:

from intelligentML import handle_duplication
url = "example.csv"
NoDupDataset = handle_duplication(url)
print(f"Dataset Without Duplications: \n{NoDupDataset}")Example-3# test.py:

from intelligentML import dataPreprocessing
url = "example.csv"
PreproccessedDataset = dataPreprocessing(url)
print(f"Preprocessed Dataset: \n{PreproccessedDataset}")Run the following Script.python test.pyNoteIt is a very tiny package according to the large ML areas. I will update the package till to advance.
Package: intelligent-pigeon
Intelligent Pigeon is a program made form IOT, by which you can detect Object from a Image and send to a server.Command for extracting images from a video:python pigeon.py extract video_path output_pathAfter running the code output_path will created with 4 subfolder on your current folder, and image extracted on those folders.Command for detect object and send it to server is:python pigeon.py send <server_name> <path> weights classes.txt labeledserver_nameis where you want to send object detected image.pathis the path from where you want detect and send images.weightsis darknet trained file for yolov4 object detection which you have to download.classes.txtis a text file where list of all object present.labeledis optional. If you want to send The image to the server with label then write it.
Package: intelligent-plant
Python App Store API ClientA python implmentation of the Intelligent Plant industrial appstore API clientSee the example folder for example on how to use the library for authorization code and implicit OAuth grant flows.Getting startedInstalling using pippip install intelligent-plantInstalling from SourceUsing pip:pip install git+https://github.com/intelligentplant/py-app-store-apiAlternatively clone the Git repo:git clone https://github.com/intelligentplant/py-app-store-api.gitExample ScriptsThe 'example' folder contains a series of examples which demonstrate the available authentication methods and queries that you can make using this library.To install dependencies used by the example scripts runpip install -r example-requirements.txtQuerying the Industrial App StoreIn order to query the industrial app store you must register as a developer and create an app registration. Full instructions can be found here:https://wiki.intelligentplant.com/doku.php?id=dev:app_store_developersOnce you have created and app registration you will need to copy and renameconfig-example.jsontoconfig.jsonand populate theidandsecretfields with you app ID and app secret.To run the authorization code grant flow example run:python example\authorization_code_grant_flow.pyTo run the authorization code grant flow example with the PKCE extensionrun:python example\authorization_code_grant_flow_pkce.pyTo run the implicit grant flow example run:The implicit grant flow is deprecated and is disabled by defaultpython example\implicit_grant_flow.pyQuerying a local App Store Connect or Data Core nodeTo run the NTLM (windows authentication) example you will need to have a data core node available on the local network.
If you have an App Store Connect (https://appstore.intelligentplant.com/Home/DataSources) installed locally you can run the example without modification. If you are trying to authenticate with a data core node you will need to change thebase_urlvariable defined in the script to match the URL of the Data Core admin UI.Run the example using:python example\ntlm_example.pyExample NotebooksYou can find examples of how to use the library to make queries in our Jupyer Hub demo notebooks (these notebooks expect a valid Industrial App Store access token to be present in theACCESS_TOKENenvironment variable).https://github.com/intelligentplant/jupyter-hub-demo/tree/master/PythonTo use this library as part of a Jupyter Notebook join the Jupyter Hub:https://appstore.intelligentplant.com/Home/AppProfile?appId=40d7a49722f84be4986318bb5cc98cf3
Package: intelligent-scissors
Intelligent-ScissorsThe Intelligent Scissors can be used to select an image region defined by strong color-changes at the edges.You can learn more about it in papers:Intelligent Scissors for Image CompositionInteractive Segmentation with Intelligent ScissorsInstallationpip install intelligent-scissorsUsageTo use in your programfromscissors.feature_extractionimportScissorsimage=...scissors=Scissors(image)seed_x,seed_y=...free_x,free_y=...path=scissors.find_path(seed_x,seed_y,free_x,free_y)Also you can run a simple demofromscissors.guiimportrun_demofile_name='image.png'run_demo(file_name)DetailsThe current implementation includesStatic featuresDynamic featuresOn-the-fly TrainingUnrestricted graph searchOn-the-fly Training allows you to select a “good” initial-boundary segment.
However, this results in poor performance for new segments with different intensity/gradient magnitudes.
To overcome this, first try selecting a small region of the new segment to create correct dynamic features.
Package: intelligent-water-droplet
Intelligent Water Drops AlgorithmA natural river often finds good paths among lots of possible paths in its ways from
the source to destination. These near optimal or optimal paths are obtained by the actions and
reactions that occur among the water drops and the water drops with the riverbeds. The intelligent
water drops (IWD) algorithm is a new swarm-based optimisation algorithm inspired from
observing natural water drops that flow in rivers. In this paper, the IWD algorithm is tested to
find solutions of the n-queen puzzle with a simple local heuristic. The travelling salesman
problem (TSP) is also solved with a modified IWD algorithm. Moreover, the IWD algorithm is
tested with some more multiple knapsack problems (MKP) in which near-optimal or optimal
solutions are obtained.See here:The intelligent water drops algorithmInstallationClone the repository and install using:python3 setup.py installorUsing pip install:pip install intelligent-water-dropletUsageDependenciesnumpyLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for details
Package: intellimouse-ctl
intellimouse-ctlA cross-platform command line tool and library for Microsoft's IntelliMouse devices.It supports the following models:Pro IntelliMouse (2019)Classic IntelliMouse (2017)UsageCLIintellimouse-ctl--help
usage:intellimouse-ctl[-h][--json]{get,set,list}...

positionalarguments:{get,set,list}getgetthevalueofasettingsetsetthevalueofasettinglistliststheconnecteddevicesandtheirindices

options:-h,--helpshowthishelpmessageandexit--jsonoutputJSONCommands passed to a device that doesn't support the given commands will ignore them.LibraryfromintellimouseimportClassicIntelliMousefromintellimouseimportProIntelliMousewithClassicIntelliMouse.enumerate()[0]asmouse:print(mouse)withProIntelliMouse.enumerate()[0]asmouse:print(mouse)FeaturesPro IntelliMouseDPILODLEDPolling RateCustom Button MappingCustom LOD CalibrationClassic IntelliMouseDPICustom Button MappingInstallPyPIpipinstallintellimouse-ctlLocalgitclonehttps://github.com/k-visscher/intellimouse-ctl.gitcdintellimouse-ctl
pipinstall.DevelopmentTo set up a virtual environment to further develop this tool/library, use the following commands:python-mvenvvenvsourcevenv/bin/activate
pipinstall--editable.DocumentationViewpdoc./src/intellimouse/Generatepdoc./src/intellimouse/-o./docsSponsors@madslfor donating the Classic IntelliMouse to this project.LicenseThis application is licensed under the theMIT license.DisclaimerAll company, product and service names used in this project are for identification purposes only.Use of these names, trademarks and brands does not imply endorsement.In no way is this project published by, affiliated with, or sponsorsed, or endorsed, or approved by Microsoft.All product names, trademarks and registered trademarks are property of their respective owners.Microsoft Pro IntelliMouse is a registered trademark or trademark of Microsoft Corporation in the United States and/or other countries.Microsoft Classic IntelliMouse is a registered trademark or trademark of Microsoft Corporation in the United States and/or other countries.All other trademarks cited herein are the property of their respective owners.
Package: intellino
The Intellino core logic wrapper for ML simulation with intellinoTable of ContentsAbout the ProjectGetting StartedPrerequisitesInstallationUsageLicenseContactAbout The ProjectSoC Platform Lab in Seoultech, South Korea made SoC chip called "Intellino". Intellino designed to working AI algorithms like aa classification task.Sometimes people feels to hard using directly board combined SoC chip. This Project for people as feels hard using board combined SoC chip support simulation built using pythonGetting StartedIntellino package can install from PyPi simplyPrerequisitespython3.6Installationpip3installIntellinoUsageMNIST classificationMNIST contains 70,000 images of handwritten digits: 60,000 for training and 10,000 for testing. The images are grayscale, 28x28 pixels. many AI framework use MNIST classification as example code.importmatplotlib.pyplotaspltfromintellino.core.neuron_cellimportNeuronCellsfromintellino.utils.data.dataloaderimportDataLoaderfrommnist_datasetimportMNISTdeftest(neuron_cells,is_log=False):test_mnist=MNIST(root="./",train=False,download=False)test_dataloader=DataLoader(test_mnist,shuffle=True)TP=0foridx,(image,target)inenumerate(test_dataloader):prediction=neuron_cells.inference(image)iftarget==prediction:TP+=1ifis_log:print(f"label->{target}, prediction->{prediction}")print("Accuracy :{}".format(TP/len(test_mnist)))deftrain():train_mnist=MNIST(root="./",train=True,download=False)train_dataloader=DataLoader(train_mnist,shuffle=True)number_of_neuron_cells=100length_of_input_vector=256neuron_cells=NeuronCells(number_of_neuron_cells=number_of_neuron_cells,length_of_input_vector=length_of_input_vector,measure="manhattan")print(f"Number of Cells :{len(neuron_cells)}")each_clss_distrib={"0":0,"1":0,"2":0,"3":0,"4":0,"5":0,"6":0,"7":0,"8":0,"9":0,}finish_train=Falseprint("Start Training!")foridx,(image,target)inenumerate(train_dataloader):iffinish_train:breakeach_clss_distrib[str(target)]+=1finish_train=neuron_cells.train(vector=image,target=target)print("Done!")returnneuron_cellsif__name__=="__main__":# Dataset Visualizationmnist_tets=MNIST(root="./",train=True,download=False)image,target=mnist_tets.__getitem__(0)image=image.reshape(16,16)plt.figure()plt.imshow(image)plt.title(str(target))plt.show()# Train NeuronCellsneuron_cells=train()test(neuron_cells,is_log=False)NeuronCellsNeuronCellsclass is core in Intellino. we just focus on here:number_of_neuron_cells: Intellino has cells. concept is similar with model in other AI framework like PyTorch, TensorFlowlength_of_input_vector: cells can split each cell and each cell have data container and data container has limit legnthFor more examples, please refer to theDocumentationLicenseDistributed under the GPL-3.0 License. SeeLICENSEfor more information.ContactMartin Hwang -hwangdonghyun@seoultech.ac.kr