spykes | Tools for spike data analysis and visualization
kandi X-RAY | spykes Summary
kandi X-RAY | spykes Summary
spykes is a Python library. spykes has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install spykes' or download it from GitHub, PyPI.
Almost any electrophysiology study of neural spiking data relies on a battery of standard analyses. Raster plots and peri-stimulus time histograms aligned to stimuli and behavior provide a snapshot visual description of neural activity. Similarly, tuning curves are the most standard way to characterize how neurons encode stimuli or behavioral preferences. With increasing popularity of population recordings, maximum-likelihood decoders based on tuning models are becoming part of this standard. Yet, virtually every lab relies on a set of in-house analysis scripts to go from raw data to summaries. We want to improve this status quo in order to enable easier sharing, better reproducibility and fewer bugs. Spykes is a collection of Python tools to make the visualization and analysis of neural data easy and reproducible. For more, see the documentation.
Almost any electrophysiology study of neural spiking data relies on a battery of standard analyses. Raster plots and peri-stimulus time histograms aligned to stimuli and behavior provide a snapshot visual description of neural activity. Similarly, tuning curves are the most standard way to characterize how neurons encode stimuli or behavioral preferences. With increasing popularity of population recordings, maximum-likelihood decoders based on tuning models are becoming part of this standard. Yet, virtually every lab relies on a set of in-house analysis scripts to go from raw data to summaries. We want to improve this status quo in order to enable easier sharing, better reproducibility and fewer bugs. Spykes is a collection of Python tools to make the visualization and analysis of neural data easy and reproducible. For more, see the documentation.
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spykes has a low active ecosystem.
It has 82 star(s) with 34 fork(s). There are 13 watchers for this library.
It had no major release in the last 12 months.
There are 10 open issues and 47 have been closed. On average issues are closed in 25 days. There are 2 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of spykes is 0.3.dev0
Quality
spykes has 0 bugs and 0 code smells.
Security
spykes has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
spykes code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
spykes is licensed under the MIT License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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spykes releases are not available. You will need to build from source code and install.
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.
spykes saves you 1095 person hours of effort in developing the same functionality from scratch.
It has 2479 lines of code, 100 functions and 35 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed spykes and discovered the below as its top functions. This is intended to give you an instant insight into spykes implemented functionality, and help decide if they suit your requirements.
- Plot population pressure
- Calculate the pstth percentile of each neuron
- Calculate the PSTH of the rasters
- Plot a histogram of an event
- Generate rasters based on conditions
- Load a numpy array of pixel data
- Loadalpixels data
- Returns the path to the data directory
- Returns the home directory
- Checks whether the argument is valid
- Fit the model
- Gradient of theta loss function
- Compute exponential exp
- Tuning function
- R Return a list of reaching_distance
- Load thereaching data
- Compute the score of the model
- Plot heat map
- Load reward data
- Create a pandas dataframe from raw data
- Find velocities in time range
- Loadingreaching data
- Splits a list of datasets
- Display firing rate
- Decode the feature vector
- Calculate the psth plot
- Loads the spikefinder data
- Load data from dual phase3
- Simulate features of the model
- Compute the PSTH for a given event
- Plot a histogram
- Calculate rasters based on conditions
Get all kandi verified functions for this library.
spykes Key Features
No Key Features are available at this moment for spykes.
spykes Examples and Code Snippets
No Code Snippets are available at this moment for spykes.
Community Discussions
No Community Discussions are available at this moment for spykes.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install spykes
Spykes can be installed using. For more detailed installation options, see the documentation.
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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