Saravanan666/pypi-org huggingface dataset:
Package: pynq
PYNQ is an open-source project from Xilinx that makes it easy to design embedded systems with Zynq All Programmable Systems on Chips (APSoCs). Using the Python language and libraries, designers can exploit the benefits of programmable logic and microprocessors in Zynq to build more capable and exciting embedded systems.
PYNQ users can now create high performance embedded applications withparallel hardware executionhigh frame-rate video processinghardware accelerated algorithmsreal-time signal processinghigh bandwidth IOlow latency controlSee thePYNQ webpagefor an overview of the project, and finddocumentation on ReadTheDocsto get started.Precompiled ImageThe project currently supportsmultiple boards.You can download a precompiled image, write the image to a micro SD card, and boot the board from the micro SD card.Quick StartSee theQuickstart guidefor details on writing the image to an SD card, and getting started with a PYNQ-enabled board.Python Source CodeAll Python code for thepynqpackage can be found in the/pynqfolder. This folder can be found on the board after the board boots with the precompiled image.To update your PYNQ SD card to the latestpynqpackage, you can run the following command from a terminal connected to your board:sudo pip3 install --upgrade --upgrade-strategy only-if-needed pynqThe--upgrade-strategy only-if-neededoption will upgrade dependencies only in case they do not satisfy the requirements, which will speed-up the installation process and also avoid possible upgrade errors.SDK software projects and Python-C source codes are also stored along with the Python source code. After installing thepynqpackage, the compiled target files will be saved automatically into thepynqpackage.Board Files and OverlaysAll board related files including Vivado projects, bitstreams, and example notebooks, can be found in the/boardsfolder.In Linux, you can rebuild the overlay by runningmakein the corresponding overlay folder (e.g./boards/Pynq-Z1/base). In Windows, you need to source the appropriate tcl files in the corresponding overlay folder.Alveo supportStarting from PYNQ version2.5.1, Alveo support has also been introduced. It is now possible to use PYNQ to tap into the potential of hardware acceleration in the data center space.To get PYNQ on an Alveo-enabled system, simply install it through PIP:pip install pynqFor Alveo cards, PYNQ currently requires aXilinx Runtime (XRT)version above or equal to2.3to be installed in the system. In terms of Operating System, any XRT-supported version of either RedHat/CentOS or Ubuntu can be used.For more information, please see the Alveogetting started guide.ContributeContributions to this repository are welcomed. Please refer toCONTRIBUTING.mdfor how to improve PYNQ.SupportPlease ask questions on thePYNQ support forum.LicensesPYNQLicense:BSD 3-Clause LicenseXilinx Embedded SWLicense:Multiple License FileDigilent IPLicense:MIT LicenseSDBuild Open Source ComponentsLicense and Copyrights InfoTAR/GZIPOpen Components Source CodeTAR/GZIP
Package: pynq-alveo-examples
This project includes introductory examples for usingPYNQwith Alveo. It requirespynqversion2.5.1and
above to work.Refer to theREADMEin theoverlaysfolder for more information regarding the used overlays and
how they are created.Please notice that not all overlays might be available for all target devices.
Supported devices are listed in the overlaysREADME.
There you may also find instructions on how to synthesize and use overlays for
a different device.Quick StartInstall thepynq-alveo-examplespackage usingpip:pipinstallpynq-alveo-examplesAfter the package is installed, to get your own copy of all the notebooks
available run:pynqget-notebooksYou can then try things out by doing:cdpynq-notebooks
jupyternotebookThere are a number of additional options for thepynq get-notebookscommand,
you can list them by typingpynqget-notebooks--helpYou can also refer to the officialPYNQ documentationfor more information
regarding thePYNQ Command Line Interfaceand in particular theget-notebookscommand.
Package: pynq-atk
本PYNQ版本是在Xilinx PYNQ版本2.5.4的基础上添加了对OV5640的支持、以及LCD 输出的支持（目前只支持正点原子的PYNQ镜像）。PYNQ is an open-source project from Xilinx that makes it easy to design embedded systems with Zynq All Programmable Systems on Chips (APSoCs). Using the Python language and libraries, designers can exploit the benefits of programmable logic and microprocessors in Zynq to build more capable and exciting embedded systems.
PYNQ users can now create high performance embedded applications withparallel hardware executionhigh frame-rate video processinghardware accelerated algorithmsreal-time signal processinghigh bandwidth IOlow latency control
Package: pynqc
pynqc NQC wrapperpynqc is a python wrapper for the NQC executable.
It utilizes the subprocess module for communitcation with NQC.
The wrapper is not complete, but should be easy to update.RequirementsThe package does not include the NQC executeable it has to be built and
installed fromhttps://github.com/jverne/nqc/There is also Dockerfile in this repo which downloads source and a patch to
enable usb support for nqc.https://github.com/jverne/nqc/also provides the firmware needed to be flashed
onto the RCX device for running progams.
The Dockerfile in the repo downloads those aswell.Layoutpynqc consists of two modules:nqc and the Nqc classraw_functions and the RawFunctions class, enables us to execute op_codes onthe rcx from the host.
It does so by generating the instruction and calls the Nqc objects exec_raw
which essentially executes nqc -Susb:/some/path/to/legotower -rawExample usageimporttimeimportpynqcfrompynqcimportraw_functionsasrfcompilation_flags=["-DOVERRIDE",f"-DVERSION={1}",f"-DID={189}"]# An nqc instance requires knowledge of where it can find the lego IR towernqc=pynqc.Nqc(serial_type="usb",device="/dev/usb/legousbtower0")# Raw functions also requires access to the lego IR tower# Therefore the nqc module has utility function which provides# an initialized raw_functions objectraw_func=nqc.get_raw_functions()try:_=raw_func.is_alive()exceptIOErroraserr:iferr.errno==255:print(err.strerror)else:_=nqc.flash_firmware("./firm0309.lgo")_=nqc.flash_and_exec_application("../tests/test.nqc",compilation_flags)time.sleep(1)raw_func.set_motor_state(rf.MOTOR_A,rf.IMMEDIATE,rf.MOTOR_ON)time.sleep(1)raw_func.set_motor_state(rf.MOTOR_A,rf.IMMEDIATE,rf.MOTOR_OFF)CreditKekoa Proudfoot :http://www.mralligator.com/rcx/This site helped a lot when implementing the raw functions from the documented
op_codes.
Package: pynq-cdma
PYNQ-CDMA-DriverPYNQ Driver for Xilinx Central Direct Memory Access IPInstallationpipinstallpynq_cdmaUsageCurrently, we only support simple DMA transferAuto bindFirst importCDMAfrompynq_cdmaimportCDMANow if your design contains CDMA IP, Pynq will automatically bind it to aCDMAinstance.You can check its status by accessing theidleproperty:frompynqimportOverlayol=Overlay('top.bit')print(ol.axi_cdma_0.idle)Data transferimportnumpyasnpfrompynqimportallocateinput_buffer=allocate(shape=(8,),dtype=np.uint8)output_buffer=allocate(shape=(8,),dtype=np.uint8)foriinrange(8):input_buffer[i]=iol.axi_cdma_0.transfer(input_buffer,0xF000_0000)ol.axi_cdma_0.transfer(0xF000_0000,output_buffer)print(output_buffer)
Package: pynq-compute-labs
This package contains the notebooks for the PYNQ portion of the Xilinx Vitis tutorials.Installing the RuntimeIf you are working through the labs as part of larger AWS workshop you will already have done this step and can move on to the "Installing Anaconda" step.for other Alveo installs you will need to source the XRT setup script:# Alveo systems onlysource/opt/xilinx/xrt/setup.shFor F1 you will need to download and source the vitis runtime script# Amazon AWS F1 system onlygitclonehttps://github.com/aws/aws-fpga.gitsourceaws-fpga/vitis_runtime_setup.shInstalling AnacondaWe recommend using PYNQ and Jupyterlab in an Anaconda environment. Open a terminal and run the following commands:wgethttps://repo.anaconda.com/archive/Anaconda3-2022.05-Linux-x86_64.sh-Oanaconda3.sh
bashanaconda3.sh-bsource~/anaconda3/bin/activateInstalling PYNQ and the Lab MaterialWith the Anaconda environment initialized, PYNQ and the lab material can be installed usingpipand thepynq get-notebookscommand.pipinstallpynq
pipinstallpynq-compute-labs
pynqget-notebooksFinally change into the newly created directory and launch JupyterLabcdpynq-notebooks
jupyterlabThis will bring up a Jupyter Lab environment you can use to complete the lab.Companion VideosThe introductory presentation to the lab material is availableon YouTubeIn addition, as labs can only show so much of PYNQ, we've created three short companion videos that cover topics that aren't addressed here. We recommending watching them in order, one after each notebook.Using Multiple DevicesHardware EmulationPackaging Your Designs
Package: pynq-dpu
This repository holds the PYNQ DPU overlay. Specifically, the Vitis AI DPU is included in the accompanying bitstreams with example training and inference notebooks ready to run on PYNQ enabled platforms. Steps are also included to rebuild the designs in Vitis and can be ported onto PYNQ-enabled Zynq Ultrascale+ boards.This release of DPU-PYNQ supports PYNQ 3.0 and Vitis AI 2.5.0.Board Support
Package: pynq-emulator
# led-matrix-emulator
About the original PYNQ by Xilinx:https://www.pynq.ioPython library made for PYNQ emulations for TUNI Students’ PYNQ-exercises
Emulator made by Riku Salminen. I don’t own the library I am trying to simulate.
I can’t promise full accuracy in the simulations, but I hope this is helpful for
at least testing purposes! Read documentation for more:https://riku-s.github.io/led-matrix/
Package: pynq-fccm-2020
This package contains the notebooks for the PYNQ portion of the Xilinx FCCM 2020 tutorial.Installing the RuntimeOn Nimbix the Xilinx runtime will already have been installedfor other Alveo installs you will need to source the XRT setup script:# Alveo systems only
source /opt/xilinx/xrt/setup.shFor F1 you will need to download and source the vitis runtime script# Amazon AWS F1 system only
git clone https://github.com/aws/aws-fpga.git
source aws-fpga/vitis_runtime_setup.shInstalling AnacondaWe recommend using PYNQ and Jupyterlab in an Anaconda environment open a terminal and run the following commandswget https://repo.anaconda.com/archive/Anaconda3-2019.07-Linux-x86_64.sh
bash Anaconda3-2019.07-Linux-x86_64.sh -b
source ~/anaconda3/bin/activateInstalling PYNQ and the Lab MaterialWith Anaconda on initialized PYNQ and the lab material can be installed usingpipand the PYNQget-notebookscommand.pip install pynq
pip install pynq-fccm-2020
pynq get-notebooks
cd pynq-notebooksFinally change into the newly created directory and launch JupyterLabjupyter labThis will bring up a Jupyter Lab environment you can use to complete the lab.Companion VideosThe introductory presentation to the lab material is availbleon YouTubeiIn addition, as labs can only show so much of PYNQ, we've created three short companion videos that cover topics that aren't addressed here. We recommending watching them in order, one after each notebook.Using Multiple DevicesHardware EmulationPackaging Your Designs
Package: pynq-helloworld
This repository contains a "Hello World" introduction application to the Xilinx PYNQ framework.The design illustrates how to run a resizer IP to resize an image on the FPGA. There are two notebooks that illustrate the resize operation. One notebook shows the image resizing done purely in software using Python Image Library. The second notebook shows the resize operation being performed in the programmable logic using a resizer IP from the  Xilinx xfopencv library.https://github.com/Xilinx/xfopencv/tree/master/examples/resizeQuick StartOpen a terminal on your PYNQ board and run:sudo pip3 install pynq-helloworld --no-build-isolationCurrently this repository is compatible withpynqpackage v2.7.0.Go to your jupyter home folder (on edge boards, this is/home/xilinx/jupyter_notebooks), and run the following to deliver the notebooks:pynq get-notebooks pynq-helloworld -p .The-poption specifies the target folder location. Then you should be
able to try the notebooks!Supported BoardsCurrently this repository is supporting:Zynq-7000 boards: Pynq-Z1, Pynq-Z2, etc.Zynq Ultrascale boards: KV260, Pynq-ZU, Ultra96, ZCU104, etc.PCIE Alveo cards: U200, U250, U280.AWS F1 instance: VU9P.For AWS, a few additional steps are required to generate the*.awsxclbinfile. For more information, you can check theREADME.mdinsideboards/VU9P/resizer.LicensePYNQLicense :BSD 3-Clause License
Package: pynqmetadata
version 0.1.5PYNQ-Metadata is an open-source project from Xilinx and is part of the PYNQ ecosystem. It's aims are to provide an abstract description of reconfigurable system designs. It is currently used internally within PYNQ to represent the hardware design currently configured in the Programmable Logic of Zynq-based devices. It is currently in pre-release.Quick StartPYNQ-Metadata can parse the design of a system in the following formats:A HWH fileAn XSA fileA JSON PYNQ-Metadata descriptionTo install PYNQ-Metadata use the following command:python3 -m pip install pynqmetadataTo parse a design use the following python commands:frompynqmetadata.frontendsimportMetadatamd=Metadata('xsa_file.xsa')md=Metadata('hwh_file.hwh')md=Metadata('pynq_metadata_json_file.json')Once a design has been parsed it can then be easily walked, searched, modified, extended, and much more.TutorialsComing soon:Documentation on how to use PYNQ-Metadata to manipulate and inspect designs.Python Source CodeAll python code for thePYNQ-Metadatapackage can be found in the/pynqmetadatafolder.pynqmetadata/frontends-- contains the frontend parsers for taking an XSA, HWH, or more into the PYNQMetadata format.pynqmetadata/models-- contains the class hierarchy for the internal object model of a PYNQMetadata representation.pynqmetadata/errors-- contains the exception classes.Changelogv0.1.5 : Prevent parsing error when external AXI ports are present in the design.v0.1.4 : Fixing runtime metadata views for when the PS is contained within a hierarchy. Relaxing the Python version constraint (for x86).v0.1.3 : Fixing python version requirementv0.1.2 : Fixed issue where custom IP was being interpreted as an empty hierarchyv0.1.1 : Fixed circular dependency issue with PYNQ-Utils
Package: pynqutils
version 0.1.1PYNQ-Utils is a repository containing utilities used in various other repos and projects across the PYNQ ecosystem. The tools are categorized into three sections:/runtimefor utilities that are used in the PYNQ runtime, such asReprDictwhich is used to pretty print dictionaries in Jupyter./setup_utilsfor utilities that are used in the construction of python packages and to help in managing the PYNQ environment./build_utilsfor utilities that are mainly used at build time whenconstructing Overlays. However, there are some utilities here that overlap with other sections, such as the XSAParser, which is also used by the PYNQ runtime for parsing the design metadata and bitstream.QuickStartTo install PYNQ-Utils use the following command:python3-mpipinstallpynqutilssetup.py exampleOne place where PYNQ-Utils is frequently used is in thesetup.pyof projects that build upon PYNQ.frompynqutils.setup_utilsimportbuild_py,find_version,extend_package,get_platformdata_files=[]extend_package(path=os.path.join(module_name,"notebooks"),data_files=data_files)setup(name="pynq_helloworld",version=find_version('{}/__init__.py'.format("pynq_helloworld")),description="PYNQ example design supporting edge and PCIE boards",long_description="PYNQ HelloWorld example design",long_description_content_type='text/markdown',author='Xilinx PYNQ Development Team',author_email="pynq_support@xilinx.com",url='https://github.com/Xilinx/PYNQ-HelloWorld.git',license='BSD 3-Clause License',packages=find_packages(),package_data={"":data_files,},python_requires=">=3.6.0",install_requires=["pynqutils","matplotlib","ipython"],entry_points={"pynq.notebooks":["pynq-helloworld ={}.notebooks.{}".format("pynq_helloworld",get_platform())]},cmdclass={"build_py":build_py})As can be seen from the above snippet PYNQ-Utils contains lots of handy utilities for creating asetup.pyscript. Some examples from above are:find_version- used to find the verstion of the current project from the sourceextend_package- used to search directories to find files and directories to add to the package.get_platform- used to determine the current platform that the project is being installed onto, for instance,edgeorpcie.A complete example of asetup.pyusing PYNQ-Utils can be foundhere.
Package: pynrl1
pynrl1L1 5G NR 3GPP Python implementation.InstallationYou need Python 3.9+ and some additional math packages like numpy and so on.pypipipinstallpynrl1Sourcesgitclonehttps://github.com/vborchsh/pynrl1cdpynrl1pipinstall.TestingAt the moment, most part of code covered by MATLAB + pytest.LimitationsThe only anthenna port assumedAny L2 procedures are out of scopeContributionTODO listNothing exceptional: fork -> edit -> pull request
Package: pynrm
pynrmpynrmis a lightweight and extensible Python library for livestock breeding simulation.OverviewThe numerator relationship matrix describes additive genetic relationships within a population.
Numerous evaluation-selection systems have been devised to produce populations with favorable genetic responses while maintaining moderate to low rates of inbreeding.pynrmprovides a simple yet powerful simulation tool to forecast the stochastic impacts of these systems.
One major bottleneck to running these simulations is that as the number of animals bred increases, the size of the matrix grows exponentially.pynrmefficiently solves for the numerator relationship matrix values by tracing up the pedigree for only the relevant ancestors, thereby minimizing computational overhead.Basic UsageInstallationpynrmis available on PyPI:$pipinstallpynrmSupported FeaturesLivestock reproduction simulations that provide fine-grained controlfrompynrmimportPedigree,Simulator# Create a simulator instancesimulator=Simulator(Pedigree(50,50),5,20,0.6,0.0)# Reproduce 4 generationsforiinrange(4):simulator.reproduce()Data visualization and analysis of simulation results# Plot average inbreeding coefficients by generationsimulator.plot_inbreeding_by_gen()# Plot average EBV by generationsimulator.plot_ebv_by_gen()# Export generated pedigree data as csvsimulator.export_to_csv("pedigree.csv")DocumentationOfficial documentation is availablehere.ContributionContributions are welcome! There are many ways to contribute to this project. Get startedhere.
Package: pynrose
pynrose - P3 Penrose Tiling GeneratorThis is a python library and stand-alone program to generate P3 penrose
tilings.Installationpip install pynroseStand-alone programAs a stand-alone program, this is able to generate P3 penrose tilings
to SVG. It supports a two modes of operation.The SVG mode outputs each rhombus as a separate closed path, with
different styles applied to thick and thin rhombii. This is intended
for visual/display applications.The SVGLINE mode outputs each rhombus edge as a separate path, and
ensures the edges are deduplicated, to avoid re-cutting/etching the
same line multiple times, for CNC/laser etching types of applications.In both modes, the tiling can be split up into multiple smaller tilings
that can be recombined with no overlaps or missing rhombii. This allows
a tiling to be split up into multiple smaller parts, based on the
constraints of the manufacturing process, and then assembled into a single
large tiling.CLI UsageTo generate a random penrose tiling as an SVGpynrose > tiling.svgTo explore more of the options that are available, you can use --help or -?pynrose --helpAPI UsageThis also exposes an API that can be used to generate P3 penrose tilings programatically
for any other use you may have.The API documentation can be found athttps://pynrose.readthedocs.iofrompynroseimportTiling,Grid,Vector# generate a new tiling with random offsetstiling=Tiling()grid=Grid(Vector(0,0),Vector(20,20))# iterate over all rhombii whose midpoints are in the grid cell from (0, 0) to (20, 20)forrhombusintiling.rhombii(grid.cell(0,0)):print(rhombus.vertices)Generation AlgorithmThe generation algorithm is based on the de Bruijn method, where there are 5 families
of equally spaced parallel lines, and each line intersection represents a
rhombus in the penrose tiling.You can read more athttps://www.mathpages.com/home/kmath621/kmath621.htmhttp://www.ams.org/publicoutreach/feature-column/fcarc-ribbons
Package: pynrrd
pynrrdpynrrd is a pure-Python module for reading and writingNRRDfiles into and
from numpy arrays.Requirementsnumpynptypingtyping_extensionsv1.0+ requires Python 3.7 or above. If you have an older Python version, please install a v0.x release instead.InstallationInstall via pip and PyPi repository (recommended)pipinstallpynrrdInstall via pip and GitHubpipinstallgit+https://github.com/mhe/pynrrd.gitInstall from source (recommended for contributing to pynrrd)For developers that want to contribute to pynrrd, you can clone the pynrrd repository and install it using the following commands:gitclonehttps://github.com/mhe/pynrrd.gitcdpynrrdpipinstall.or, for the last line, instead use:pipinstall-e.to install in ‘develop’ or ‘editable’ mode, where changes can be made to the local working code and Python will use
the updated pynrrd code.TestsThe tests can be run via the following command from the base directory:python-munittestdiscover-vnrrd/testsFormat and Lint codeThis repository uses pre-commit hooks to run format and lint the code and they are enforced in CI. See [pre-commit](https://pre-commit.com)Example usageimportnumpyasnpimportnrrd# Some sample numpy datadata=np.zeros((5,4,3,2))filename='testdata.nrrd'# Write to a NRRD filenrrd.write(filename,data)# Read the data back from filereaddata,header=nrrd.read(filename)print(readdata.shape)print(header)Next StepsFor more information, see thedocumentation.LicenseSee theLICENSEfor more information.
Package: pyns
pyNS 🌲pyNSis a python library to easily interact with the Neuroscout API.pyNSenables advanced used cases not supported by theneuroscout.orgweb-based analysis builder, such as batch-creation of analyses, meta-analytic applications, as well as supporting the operations of theneuroscout-cliInstallationUsepipto install it:pip install pynsDocumentation 📚Full documentation can be found atreadthedocs.For API docs, check out theSwagger API UI.For a tutorial see thisjupyter notebook example.You can also follow the Neuroscout Paper's analyses in this interactivejupyter book.TestingWe use pytest for testing, and betamax to record HTTP requests used in tests.To re-run tests locally set theUSER_TEST_EMAILandUSER_TEST_PWDenvironment variables with valid API credentials.
Package: pyNSC
pyNSCpyNSC is a Python package for nonnegative sparse coding (NSC).To get started using these components in your own software, please go to the
repositoryREADME.LicensepyNSCis licensed under the terms of the MIT license. See the file
“LICENSE” for information on the history of this software, terms & conditions
for usage, and a DISCLAIMER OF ALL WARRANTIES.All trademarks referenced herein are property of their respective holders.Copyright (c) 2018–, Michael Beyeler, University of Washington
Package: pynsca
A very simple module to allow nagios service check results to be submitted via
NSCA.Usage>>> import pynsca
>>> from pynsca import NSCANotifier
>>> notif = NSCANotifier("nagios")
>>> notif.svc_result("host", "service", pynsca.OK, "Looks Good!")Prebuild RPM packagesThe Bareos project builds (using open build server) and publishes python-pynsca RPM packages for a
variety of RPM based Linux distribution, you can directly install from the repository:http://download.bareos.org/bareos/contrib/Debian / Ubuntu packages may follow later.RequirementsPython 2.4 or higherpython-mcrypt, if using AES encryptionpycrypto, if using 3DES encryptionNo other libraries requiredIssuesPlease file any bugs or feature requests athttps://github.com/djmitche/pynsca/issuesChanges1.6 (Unreleased)Debian package updated.spec file to generate a RPM package.1.5PyCrypto is only required when using 3DES encryption (mode 3).1.4The library now supports 3DES encryption (mode 3).  This adds the requirement
for PyCrypto.