quickspikes | Fast spike detection and extraction for Python | Machine Learning library

by melizalab Python Version: 2.0.2 License: GPL-3.0

X-Ray Key Features Code Snippets(1)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | quickspikes Summary

quickspikes is a Python library typically used in Artificial Intelligence, Machine Learning applications. quickspikes has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can install using 'pip install quickspikes' or download it from GitHub, PyPI.

This is a very basic but very fast window discriminator for detecting and extracting spikes in a time series. It was developed for analyzing extracellular neural recordings, but also works with intracellular data and probably many other kinds of time series.

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Quality

Security

License

Reuse

Support

quickspikes has a low active ecosystem.

It has 16 star(s) with 5 fork(s). There are 7 watchers for this library.

There were 1 major release(s) in the last 12 months.

There are 1 open issues and 4 have been closed. On average issues are closed in 212 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of quickspikes is 2.0.2

Quality

quickspikes has 0 bugs and 0 code smells.

Security

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

quickspikes code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

quickspikes is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

quickspikes releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

quickspikes saves you 47 person hours of effort in developing the same functionality from scratch.

It has 124 lines of code, 15 functions and 5 files.

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

Top functions reviewed by kandi - BETA

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

Returns the energy of a spike
Extract spike and lag from spike positions

Get all kandi verified functions for this library.

quickspikes Key Features

No Key Features are available at this moment for quickspikes.

quickspikes Examples and Code Snippets

quickspikes,Installation and Use

Python

Lines of Code : 14

License : Strong Copyleft (GPL-3.0)

Copy

pip install quickspikes

pip install .

import quickspikes as qs
det = qs.detector(1000, 30)
times = det.send(samples)

for chunk in my_data_generator():
    times = det.send(chunk)
    # process times

reldet = qs.detector(2.5, 30)
reldet.scale_thre

Community Discussions

Trending Discussions on quickspikes

De-spiking a non-periodic signal

QUESTION

De-spiking a non-periodic signal

Asked 2018-Feb-13 at 15:33

I am working on a set of data (x={time},y={measure}) that comes out from an instrument, but sometimes the source cause a spike on data, which cause an incorrect plot and can cause mistakes in calculating features like max and min.

So I need to remove these spikes from my data, for examples the spikes surrounded by the red circle in the image:

I have found this example for de-spiking but I don't know how to invert the signal (and if it's correct on a non-symmetric signal) and I think it's just for detecting the spikes and I need to remove them with operations like fitting etc...

I need help to know if there are better ways to accomplish my task or if i have simply to adapt the example above to my situation (in that case I'll need help because I have no idea how to do it).

...

ANSWER

Answered 2018-Feb-13 at 07:53

Here is a set of steps you can follow to estimate the location of peaks:

Smooth the data. Any number of filters are available for this. An excellent starting point is the smooth function described in the scipy cookbook. It will be up to you to select the appropriate parameters like window size:

Source https://stackoverflow.com/questions/48752282

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

Vulnerabilities

No vulnerabilities reported

Install quickspikes

The algorithm is written in cython. You can get a python package from PyPI:.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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