Kernels¶
Definition of a hierarchy of classes for kernel functions to be used in convolution, e.g., for data smoothing (low pass filtering) or firing rate estimation.
Base kernel classes¶
Kernel(sigma[, invert]) |
This is the base class for commonly used kernels. |
SymmetricKernel(sigma[, invert]) |
Base class for symmetric kernels. |
Symmetric kernels¶
RectangularKernel(sigma[, invert]) |
Class for rectangular kernels. |
TriangularKernel(sigma[, invert]) |
Class for triangular kernels. |
EpanechnikovLikeKernel(sigma[, invert]) |
Class for epanechnikov-like kernels. |
GaussianKernel(sigma[, invert]) |
Class for gaussian kernels. |
LaplacianKernel(sigma[, invert]) |
Class for laplacian kernels. |
Asymmetric kernels¶
ExponentialKernel(sigma[, invert]) |
Class for exponential kernels. |
AlphaKernel(sigma[, invert]) |
Class for alpha kernels. |
Examples¶
>>> import quantities as pq
>>> kernel1 = GaussianKernel(sigma=100*pq.ms)
>>> kernel2 = ExponentialKernel(sigma=8*pq.mm, invert=True)
