cusignal | cuSignal - RAPIDS Signal Processing Library | GPU library

by rapidsai Python Version: v23.08.00a License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | cusignal Summary

cusignal is a Python library typically used in Hardware, GPU, Numpy applications. cusignal has no bugs, it has no vulnerabilities, it has a Permissive License and it has high support. However cusignal build file is not available. You can download it from GitHub.

cuSignal is a GPU-accelerated signal processing library that is both based on and extends the SciPy Signal API. Notably, cuSignal:.

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Security

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Support

cusignal has a highly active ecosystem.

It has 675 star(s) with 120 fork(s). There are 43 watchers for this library.

It had no major release in the last 12 months.

There are 24 open issues and 129 have been closed. On average issues are closed in 47 days. There are 6 open pull requests and 0 closed requests.

It has a positive sentiment in the developer community.

The latest version of cusignal is v23.08.00a

Quality

cusignal has 0 bugs and 0 code smells.

Security

cusignal has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

cusignal code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cusignal is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

cusignal releases are available to install and integrate.

cusignal has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions, examples and code snippets are available.

cusignal saves you 4431 person hours of effort in developing the same functionality from scratch.

It has 10187 lines of code, 618 functions and 72 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed cusignal and discovered the below as its top functions. This is intended to give you an instant insight into cusignal implemented functionality, and help decide if they suit your requirements.

Compute the isotropic Fourier Transform
Return a function for a given window
F firwin 2
Calculate the fs
Generate a spectrogram
Helper function for spectra
Detrend from an array
Return the product of an iterable
Return a dict of the command class to use
Create a ConfigParser object from root
Stochastic Time Fourier Transform
Compute the smoothing of the given sos
Calculate the coherence of a time series
Calculate periodogram using Welch s method
Resample a time series
Lombscargle
R Chebyshev window
Calculate a ca_c_cfar
Read a sigmf - meta - data file
Computes the correlation between two arrays
Calculate the Bartlett transform
Convolve 2d array
R Compute the Fourier transform
Create the versioneer config file
Compute an exponential kernel
Convolve 2d image using convolution

Get all kandi verified functions for this library.

cusignal Key Features

No Key Features are available at this moment for cusignal.

cusignal Examples and Code Snippets

No Code Snippets are available at this moment for cusignal.

Community Discussions

Trending Discussions on cusignal

Alternative to scipy.lfilter

Installing cusignal on windows 10

QUESTION

Alternative to scipy.lfilter

Asked 2020-Oct-29 at 05:30

is there a way to have an alternative implementation of lfilter within scipy? I want to use the cusignal library and lfilter is not supported at the moment.

Here's my my current code that I want to speed up:

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ANSWER

Answered 2020-Oct-29 at 05:30

Even though your filter is in principle an infinite impulse response (IIR) filter, the impulse response for this particular filter decays very fast. You can compute the impulse response by running an impulse through it with lfilter like lfilter(b, a, [1] + [0]*99). Here is what I get:

As you can see, the taps are nearly zero above sample 20 or so. So you can take the first 20 samples of the impulse response to make an accurate truncated FIR approximation. From there, you can apply that FIR approximation with any FIR filtering function, like np.convolve, scipy.signal.convolve, or scipy.signal.fftconvolve.

Another thought: With any of these filtering functions, you could try casting all the args to np.float32. They might internally switch to a 32-bit float implementation that is faster than the 64-bit float implementation.

Source https://stackoverflow.com/questions/64564386

QUESTION

Installing cusignal on windows 10

Asked 2020-Sep-04 at 19:38

I wanted to install the cusignal python package on windows and I was following the instructions on the following github link.

It says to run the following commands:

...

ANSWER

Answered 2020-Sep-04 at 19:38

The instructions expect that you first clone the GitHub repository, which has such a python folder, then run those instructions, like

Source https://stackoverflow.com/questions/63746066

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install cusignal

A polyphase resampler changes the sample rate of an incoming signal while using polyphase filter banks to preserve the overall shape of the original signal. The following example shows how cuSignal serves as a drop-in replacement for SciPy Signal's polyphase resampler and how cuSignal interacts with data generated on GPU with CuPy, a drop-in replacement for the numerical computing library NumPy. This code executes on 2x Xeon E5-2600 in 2.36 sec. This code executes on an NVIDIA V100 in 13.8 ms, a 170x increase over SciPy Signal. Next, we'll show that cuSignal can be used to access data that isn't explicitly generated on GPU. In this case, we use cusignal.get_shared_mem to allocate a buffer of memory that's been addressed by both the GPU and CPU. This process allows cuSignal to process data online. This code executes on an NVIDIA V100 in 174 ms. Finally, the example below shows that cuSignal can access data that's been generated elsewhere and moved to the GPU via cp.asarray. While this approach is fine for prototyping and algorithm development, it should be avoided for online signal processing. This code executes on an NVIDIA V100 in 637 ms.
cuSignal has been tested on and supports all modern GPUs - from Maxwell to Ampere. While Anaconda is the preferred installation mechanism for cuSignal, developers and Jetson users should follow the source build instructions below; there isn't presently a conda aarch64 package for cuSignal.

Support

The complete cuSignal API documentation including a complete list of functionality and examples can be found for both the Stable and Nightly (Experimental) releases. cuSignal has about 75% coverage of the SciPy Signal API and includes added functionality, particularly for phased array systems and speech analysis. Please search the documentation for your function of interest and file an issue if you see a gap. cuSignal (Stable) | cuSignal (Nightly).

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