persim.PersImage

class persim.PersImage(*args, **kwargs)

Initialize a persistence image generator.

Parameters:

• pixels (pair of ints like (int, int)) – Tuple representing number of pixels in return image along x and y axis.

• spread (float) – Standard deviation of gaussian kernel.

• specs (dict) –

  Parameters for shape of image with respect to diagram domain. This is used if you would like images to have a particular range. Shaped like:

  {
      "maxBD": float,
      "minBD": float
  }
  

• kernel_type (string or ...) – TODO: Implement this feature. Determine which type of kernel used in the convolution, or pass in custom kernel. Currently only implements Gaussian.

• weighting_type (string or ...) – TODO: Implement this feature. Determine which type of weighting function used, or pass in custom weighting function. Currently only implements linear weighting.

Deprecated since version 0.1.5: Replaced with the class persim.PersistenceImager.

__init__(pixels=(20, 20), spread=None, specs=None, kernel_type='gaussian', weighting_type='linear', verbose=True)

Methods

__init__([pixels, spread, specs, ...])
fit_transform(X[, y])	Fit to data, then transform it.
kernel([spread])	This will return whatever kind of kernel we want to use.
set_output(*[, transform])	Set output container.
show(imgs[, ax])	Visualize the persistence image
to_landscape(diagram)	Convert a diagram to a landscape (b,d) -> (b, d-b)
transform(diagrams)	Convert diagram or list of diagrams to a persistence image.
weighting([landscape])	Define a weighting function, for stability results to hold, the function must be 0 at y=0.