Saravanan666/pypi-org huggingface dataset:
Package: ndpyramid
ndpyramidA small utility for generating ND array pyramids using Xarray and Zarr.installationNdpyramid can be installed in three ways:Using thecondapackage manager that comes with the Anaconda/Miniconda distribution:condainstallndpyramid--channelconda-forgeUsing thepippackage manager:python-mpipinstallndpyramidTo install a development version from source:gitclonehttps://github.com/carbonplan/ndpyramidcdndpyramidpython-mpipinstall-e.usageNdpyramid provides a set of utilities for creating pyramids with standardized metadata.
The example below demonstrates the usage of thepyramid_coarsenandpyramid_reprojectutilities. Check outthisJupyter Notebook for a complete demonstration.importxarrayasxrimportrioxarrayfromndpyramidimportpyramid_coarsen,pyramid_reproject# load a sample xarray.Datasetds=xr.tutorial.load_dataset('air_temperature')# make a coarsened pyramidpyramid=pyramid_coarsen(ds,factors=[16,8,4,3,2,1],dims=['lat','lon'],boundary='trim')# make a reprojected (EPSG:3857) pyramidds=ds.rio.write_crs('EPSG:4326')pyramid=pyramid_reproject(ds,levels=2)# write the pyramid to zarrpyramid.to_zarr('./path/to/write')See the docstrings for more details about input parameters and options.licenseAll the code in this repository isMITlicensed, but we request that you please provide attribution if reusing any of our digital content (graphics, logo, articles, etc.).about usCarbonPlan is a non-profit organization working on the science and data of carbon removal. We aim to improve the transparency and scientific integrity of carbon removal and climate solutions through open data and tools. Find out more atcarbonplan.orgor get in touch byopening an issueorsending us an email.
Package: ndreg
# ndreg
[![Travis](https://travis-ci.org/neurodata/ndreg.svg?branch=master)](https://travis-ci.org/#)
[![Documentation Status](https://readthedocs.org/projects/ndreg/badge/?version=latest)](http://ndreg.readthedocs.io/en/latest/?badge=latest)
[![DockerHub](https://img.shields.io/docker/pulls/neurodata/ndreg.svg)](https://hub.docker.com/r/neurodata/ndreg)
<br/>
Package that performs affine and LDDMM registrationeasily<br/>## Sytem RequirementsThe recommended way to use this package is to install [Docker](https://store.docker.com/search?offering=community&type=edition). Docker is currently available on Mac OS X El Capitan 10.11 and newer macOS releases, the following Ubuntu versions: Zesty 17.04 (LTS), Yakkety 16.10, Xenial 16.04 (LTS), Trusty 14.04 (LTS), and Windows 10.### Software Dependencies (with version numbers)The only software dependency needed if using the recommended method is Docker. The following dependencies are included in the Docker Image.External libraries: <br/>
- Insight Segmentation and Registration Toolkit (ITK) – 4.12.2Python depedencies: <br/>
- jupyter – (1.0.0)
- numpy – (1.13.3)
- scikit-image – (0.13.1)
- scikit-learn – (0.19.1)
- scipy – (1.0.0)
- SimpleITK – (1.0.1)### Versions tested on
We have tested the Docker image and build on macOS High Sierra (on MacBook Pro with 2.9 GHz Intel Core i7 and 16 GB RAM) and Ubuntu Xenial 16.04.3 LTS (with 64 GB RAM).## Installation GuideOnce Docker is installed on your machine, pull theneurodata/ndregimage from Docker Hub [here](https://hub.docker.com/r/neurodata/ndreg) as follows: <br/>docker pull neurodata/ndreg<br/>It will typically take around 3 minutes to pull the entire Docker image.## Demo### Instructions to run on dataIn order to use the functionality built into this Docker image, you need to run the Docker image:docker run -p 8888:8888 neurodata/ndreg<br/>This should print a link to the terminal console that looks like this: <br/>http://0.0.0.0:8888/?token=SOME_TOKEN<br/>Go to this link in your browser by copying and pasting it. <br/>Next, click onndreg_demo.ipynb. Once the notebook opens, you can run all cells by clicking on ‘Cell’ and then ‘Run All’.The expected run time for this demo is ~ 2 minutes.### Expected OutputThe last 3 cells in the demo notebook display images that should look the same (both the LDDMM registered image and the displacement field warped image) and a mean squared error### Congrats, you’ve succesfully run ndreg!
Package: ndrop
a File Transfer Tool. compatible “Dukto” and “NitroShare”, support “HFS”FeatureJust drop. No authentication, no authorize, use in trusted network.compatible “Dukto” and “NitroShare”support HTTP File Server (HFS)only CLI mode, no GUI.DuktoorNitroShareall provide GUI window.transfer File, Directory.Duktoalso send TEXToutput to DISK or STDOUTInstall# from pypi
pip3 install ndrop

# from source code
python3 setup.py installUsing ScenariosHTTP File Serveron Server:$ ndrop --hfs
* HTTP File Server start
* Running on http://192.168.100.2:8000/ (Press CTRL+C to quit)
* Root path: ./on Client:open http://192.168.100.2:8000/ by browserClient to Serveron Server(ndrop orDukto):$ ndrop --listen 0.0.0.0 /tmp
 * My Node: user at DESKTOP-client (Linux)
 * [Dukto] listen on 0.0.0.0:4644(tcp):4644(udp) - [127.0.0.1,192.168.0.1]
 * [NitroShare] listen on 0.0.0.0:40818(tcp):40816(udp) - [127.0.0.1,192.168.0.1]
 * Online : [Dukto] 192.168.0.10:4644 - User at DESKTOP-LU1OA8H (Windows)
 * Online : [NitroShare] 192.168.0.11:40818 - USER-4VC7CASHSL (windows)
[process bar ... ]or output to STDOUT or PIPE:$ ndrop --listen 0.0.0.0 - | mpv -on Client(ndrop,DuktoorNitroShare):$ ndrop --mode dukto --send 192.168.0.1 /tmp/100M.bin
# or
$ ndrop --mode nitroshare --send 192.168.0.1 /tmp/100M.bin
[process bar ... ]Client to Server with SSLMaybe transfer though PUBLIC network, such as Internet.Duktodo not support SSL.on Server:$ ndrop ~/cert.pem --key ~/key.pem --listen 0.0.0.0 /tmp
 * My Node: user at DESKTOP-client (Linux)
 * [Dukto] listen on 0.0.0.0:4644(tcp):4644(udp) - [127.0.0.1,192.168.0.1]
 * [NitroShare] listen on 0.0.0.0:40818(tcp):40816(udp) - [127.0.0.1,192.168.0.1]
 * Online : [Dukto] 192.168.0.10:4644 - User at DESKTOP-LU1OA8H (Windows)
 * Online : [NitroShare] 192.168.0.11:40818 - USER-4VC7CASHSL (windows)
[process bar ... ]on Client:$ ndrop --mode dukto ~/cert.pem --key ~/key.pem --send 192.168.0.1 /tmp/100M.bin
[process bar ... ]
Package: nds
Non-dominated sortingDescription of the methodYou can read about the method in the next article:Buzdalov M., Shalyto A. A Provably Asymptotically Fast Version of the Generalized Jensen Algorithm for Non-dominated Sorting  // Parallel Problem Solving from Nature XIII.- 2015. - P. 528-537. - (Lecture Notes on Computer Science; 8672)RequirementsPython 3.6 or higher.Installedsetuptools.Installedwheel.InstallationPyPILocal installationRunpip install ..TestsRun command:python -m unittest discover -v ./testsHow to useThe example:importrandom# Package must be installed.fromndsimportndomsortseq=[random.sample(range(-10,11),5)foriinrange(30)]# It is dictionary.fronts=ndomsort.non_domin_sort(seq)# Or we can get values of objectives.# fronts = ndomsort.non_domin_sort(seq, lambda x: x[:4])# 'fronts' is a tuple of front's indices, not a dictionary.# fronts = ndomsort.non_domin_sort(seq, only_front_indices=True)forfrontinfronts:print("\nFront index is{}".format(front))forseqinfronts[front]:print("\t{}".format(seq))Other implementationsJava (Jensen-Fortin-Buzdalov divide-and-conquer method)C#Example
Package: ndscope
Next-generation NDS oscilloscopendscopeis a tool for viewing time series data from the LIGO Network
Data Services (NDS).  Based onnds2-clientand the
high-performancepyqtgraphplotting library,ndscopeis a able to plot both online and offline data for many
channels simultaneously with intuitive mouse pan/zoom support.Features:online mode for raw and trend datamouse pan and zoom, with background auto-fetch of new dataautomatic transition to second/minute trend data at appropriate zoom levelstriggering, for true oscilloscope behaviorcursors and crosshair in both time and Y axessave layout templates in easy-to-use YAML formatload templates from various legacy formats, including StripTool .stp, dataviewer .xml, and .txtenhanced "StripTool mode" with auto-backfill of past dataNDS2 and NDS1 protocol supportchannel selection GUILeft mouse click+drag for pan, and right mouse click+drag for zoom
(left/right for in/out in time, and up/down for in/out in Y).
Click+drag on axis to retrict pan/zoom to only that dimension.IssuesPlease report issues to thegitlab issue tracker.RequirementsPackage requirements forndscope(Debian package names):python3-pyqtgraphpython3-nds2-clientpython3-qtpypython3-gpstimepython3-dateutilpython3-yamlpython3-cairosvgpython3-h5pyThe following packages are used for development purposes:pyqt5-dev-toolsqt5-designerpython3-setuptools_scmpytest3-pytestPre-built binary packages are available viapipandcondaand for the variousIGWN supported operating
systems.
Package: nd.semanticcore
Using of semanticcore script==============================Script treat two corpus of text to find semantic coreby them.Used:semanticcore [<OPIONS>] <SAMPLE1> <DELIMETER> <SAMPLE2>Sample:<SAMPLE1>, <SAMPLE2> is eneumeration of text files. Sample1 is targetsampling for tags (keywords) definition. Sample2 is control samping ...Switch:-d  <STRING>        Delimeter (default :::);-w  <INT>:<INT>     Weight interval to display (default 0:10);-c  <INT>           Lowest border number of word in document used to exceptwords (default 10);[name:недописано]* Sat Jul 25 2009 Andrey Orlov <dbdt@dreambot.ru> 0.1.2Zero Devision Error on empty files fixed* Tue Jul 21 2009 Andrey Orlov <dbdt@dreambot.ru> 0.1.1- Document part range now in 0-100%. Here 0 mean "no words".* Mon Jul 13 2009 Andrey Orlov <dbdt@dreambot.ru> 0.1.0- Package renamed- Some enchacements applied* Thu Oct 09 2008 Andrey Orlov <dbdt@dreambot.ru> 0.0.2- Main program and short instruction added* Tue Sep 30 2008 Andrey Orlov <dbdt@dreambot.ru> 0.0.1- first official release
Package: ndsharray
Overviewndarray + sharing = ndsharrayIntroductionThis python module let you share a numpy ndarray within different processes (either via python's multiprocessing or
sharing between different python instances). The library behind this package is the libmmapfrom official python - no extra library, except numpy, is needed.
The mmap approach is using the shared memory, which can be accessed by different CPUs/python instances. Using shared
memory is much
faster than the pickle approach - you can even do a video streaming on a Raspberry Pi / Jetson Nano over multiple
python processes.This library is easy-to-use, just initialize the shared array with a unique tag and write/read! You can even change the
numpy array dimension/shape/dtype during runtime - the mmap will be silently rebuild if there is a change in the
numpy array  size/shape/dtype.Small Example Code:importnumpyasnpfromndsharrayimportNdShArrayshared_array=NdShArray("my_unique_tag",r_w='w')# r_w must be specifiedmy_array=np.random.random((400,800))shared_array.write(my_array)print(my_array)That's all for writing into shared memory. How to read? Open a second python instance:importnumpyasnpfromndsharrayimportNdShArrayshared_array=NdShArray("my_unique_tag",r_w='r')# r_w must be specifiedstatus,my_array=shared_array.read()print(my_array)DocumentationCan be found here:https://ndsharray.readthedocs.io/en/latest/RequirementsPython â‰¥ 3.6numpyTested with example codes onWindows 10, amd64, Python 3.6 / 3.8Ubuntu 20, amd64, Python 3.6 /3.8NVIDIA Jetson Nano with Ubuntu 18.04, ARM64-bit (aarch64), Python 3.6Some technical notesThis library shall be an easy-to-use library and also shall be faster than pickling numpy arrays to another process.
Please note that the python's providedshared_memorydoes the same as ndsharray, but
is using byte array instead of numpy array! However, shared_memory is available since python 3.8 and not
supported for python 3.6.Installation from GithubMake sure to have git, python and pip in your environment path or activate your python environment.To install enter in cmd/shell:git clone https://github.com/monzelr/ndsharray.gitcd ndsharraypip install .Alternative with python:python setup.py install
Package: ndsi
Network Device Sensor Interfacendsi-commspec.mddefines theNetwork Device Sensor protocol.examples/ndsi-test-host.pyfeatures a (not so pretty) host test implementation.examples/ndsi-client-example.pyshows a simple client application.InstallationWe provide pre-compiled wheels for macOS, Windows, and Linux 64-bit architectures and
Python versions 3.6+. You can install them viapython -m pip
python -m pip install ndsiFor any other architecture or Python version, you will have to run the source installation described inSOURCE-INSTALL.md.Maintainer documentationSeeMAINTENANCE.mdfor maintainer documentation.ChangelogVersion 1.4.1Updated TurboJPEG requirement instructions for Ubuntu#65Automated wheel builds and deployment via PyPI#65Version 1.4.0 and priorPrevious changelogs can be found athttps://github.com/pupil-labs/pyndsi/releases
Package: ndsl-attn-fs-archs
Installation guideThis repository contains the developed architectures in the research of attention mechanisms as feature selectors.Currently, not all architectures are available. This packages will be updated in the future (I hope that).The code here is developed as a python package. Anyway, a magic makefile is included (for your comfort :wink:), and some specifications are included below.Installing from PyPiBy running the next line you will install it from PyPi repo:pip install ndsl-attn-fs-archsInstalling from sourceBuilding the packageTo build the package you just need to run:makeor:make buildInstalling the packageTo install the package just execute:make installRunning testsTo run the included tests just executemake testIf you want to add more tests don't forget to add them in the makefile.Easy, right? :sunglasses:
Package: ndstextgen
NDS Text GeneratorCommand line tool to render text from NDS .NFTR fonts.Run from pippip install ndstextgenndstextgen --helpRun from sourceInstall pipenv.pipenv syncpipenv run python ndstextgen --helpCommand line usagendstextgen <options> FONT "TEXT"Example:ndstextgen --color red font.NFTR "Print multiline\nred text."FONT: (Required) Filename of the font, .NFTR extension can be omitted.TEXT: (Required) Text to write. "\n" can be used for a line break. Can be the name of a UTF-8 file to read the text from.--out <file>: Output file, set empty to just return the image. Default: text.png--vert <int>: Vertical spacing between lines. Default: 2--fw <int>: Use a fixed width instead of the VWF values in the font.--spacing <int>: Additional horizontal spacing between characters.--color <text>: Color to apply to the font. Default: black--bg <text>: Background color. Default: transparent--width <int>: Set width for the generated image. Default: 256--height <int>: Set height for the generated image. Default: 256--center: Center each line.--wwrap: Automatic wordwrap.--no-crop: Don't crop the image before saving it.--encoding <str>: Encoding the font uses. SeePython documentation.Script usageimportndstextgenimage=ndstextgen.gen("font.NFTR","Print multiline\nred text.",color="red")
Package: ndtransform
This library contains 3 functions that are meant to be used for tensor transformations.
Mostly they are built using standard python library with some functionalities of numpy.The 3 functions in question:
-transpose2d(tensor): takes a tensor in the format "list[list[float]]". This tensor will
then be transposed. Returns a list.-window1d(array, size, *shift, *stride): "array" takes an array in format list or np.ndarray;
"size", of which size you want to get your windows; "shift" (default = 1) optional argument, which
determines step in between window calculations; "stride" (default = 1) optional argument, which
represents the step size. Returns a list.-convolution2d(matrix, kernel, *stride): "matrix" takes an np.ndarray that you would like to make
calculations on; "kernel" takes an np.ndarray that will be used to do calculations on original
matrix; "stride" (default = 1) optional argument that represents the step size, when generating
result. Returns np.ndarray.
Package: ndtypes
OverviewThendtypesmodule is the Python interface to thelibndtypeslibrary.libndtypesimplements dynamic types relevant for scientific computing.These types contain full memory layout information and are used both for type
checking and  accessing values in memory.Types are specified using a close variant of the datashape language.ndtypessupports other container modules.  It displays types, performs
type checking, and the concrete type layout information is used to traverse
memory.As an example,xndis a container module that is based onndtypes.Linkshttps://github.com/plures/http://ndtypes.readthedocs.io/en/latest/http://xnd.readthedocs.io/en/latest/
Package: ndu-gate-camera
ndu-gate - NDU Gate Camera ServiceThis is a service project that run on edge devices and servers to consume videos sources(camera, file etc.) and process them.InstallationUsing .deb packageGo relases and download latest .deb package and run the following command.sudo dpkg -i ./python3-ndu-gate.debBuilding .deb packagesudo ./generate_deb_package.shUsing pip packagepython3 -m pip install --upgrade ndu-gate-camerainstallation without dependenciespython3 -m pip install --upgrade --no-deps ndu-gate-cameraRunning with clindu-gate -c config-file-path.yamlAPINDUCameraRunnerIt is a interface class(api/ndu_camera_runner.py) that sould be impelemented to process source frames according to different use-cases.VideoSourceIt is a interface class (api/video_source.py) that should be implement to stream different type of video sources.CameraVideoSource- Streams frames from OS cameraFileVideoSource- Streams frames from video file.ImageVideoSource- Streams frames from image file.YoutubeVideoSource- Streams frames from youtube video url.IPVideoSource- Streams frames from IP cameraPICameraVideoSource- Streams frames from Raspberry PI cameraResultHandlersIt is the interface class (api/result_handler.py) that decides how to manage the data produced by runners.FILE      -ResultHandlerFile- Writes the data to the specified fileSOCKET    -ResultHandlerSocket- Sends the data to the specified socket connectionMQTT      -ResultHandlerMqtt- Sends the data NDU platform using MQTT Gateway APIHTTP      - ResultHandlerRequest  - TODO - Sends data to the specified service via HTTP(S)Settingsndu-gate service global settings/etc/ndu-gate/config/ndu_gate.yamlLogging settings :/etc/ndu-gate/config/logs.confSee more atNDU-GATE.YAML ConfigurationDevelopmentMore details atNDU-Gate Development HelpAdding New RunnerYou can a new implemented runner to this service.Create a folder under/var/lib/ndu_gate/runners/. This folder name should be unique.Add yourNDUCameraRunnerimplementation python file to/var/lib/ndu_gate/runners/folder.Add your runner's config file to/etc/ndu-gate/config/Then to activiate your runner, add the following settings top under instance runners collection in/etc/ndu-gate/config/ndu_gate.yamlfileinstance:
      - source
        type: CAMERA
        device: MyLapCamera # optional
        runners:
          - name: My Runner
            type:  # this should be same with <folder-name>
            configuration: <folder-name>.json # optional
            class: MyRunner # The class name your runner class
Package: ndu-gateway
ThingsBoard IoT GatewayThe ThingsboardIoT Gatewayis an open-source solution that allows you to integrate devices connected to legacy and third-party systems with Thingsboard.Thingsboard is an open-source IoT platform for data collection, processing, visualization, and device management. SeeWhat is Thingsboard?if you are new platform user.What is ThingsBoard IoT Gateway?Getting started with ThingsBoard IoT GatewayGateway featuresThingsboard IoT Gateway provides following features:OPC-UAconnectorto collect data from devices that are connected to OPC-UA servers.MQTTconnectorto collect data that is published to external MQTT brokers.Modbusconnectorto collect data from Modbus servers and slaves.BLEconnectorto collect data from BLE devices.Requestconnectorto collect data from HTTP API.CANconnectorto collect data using CAN protocol.BACnetconnectorto collect data from devices using BACnet protocol.ODBCconnectorto collect data from ODBC databases.Customconnectorto collect data from custom protocols.Persistenceof collected data to guarantee data delivery in case of network and hardware failures.Automatic reconnectto Thingsboard cluster.Simple yet powerfulmappingof incoming data and messagesto unified format.Remote logging featureto monitor the gateway status through the ThingsBoard WEB interface.RPC gateway methodsto control and get information from the gateway through ThingsBoard WEB interface.ArchitectureThe IoT Gateway is built on top ofPython, however is different from similar projects that leverage OSGi technology.
The idea is distantly similar to microservices architecture.The gateway supports custom connectors to connect to new devices or servers and custom converters for processing data from devices.Especially, when we are talking about language APIs and existing libraries to work with serial ports, GPIOs, I2C, and new modules and sensors that are released every day.The Gateway provides simple integration APIs, and encapsulates common Thingsboard related tasks: device provisioning, local data persistence and delivery, message converters and other.For processing data from devices you also can write custom converter, it will receive information from device and send it to converter to convert to unified format before sending it to the ThingsBoard cluster.SupportCommunity chatQ&A forumStackoverflowDon't forget to star the repository to show your ❤️ and support.LicensesThis project is released underApache 2.0 License.