AsinhAutomaticNorm

AsinhAutomaticNorm#

class scarlet2.plot.AsinhAutomaticNorm(observation, channel_map=None, minimum=0, upper_percentile=99.5, noise_level=1, vibrance=0.15)[source]#

Bases: AsinhNorm

AsinhAutomaticNorm class

Norm that scales as arcsinh(I / beta) with parameters chosen automatically

The turnover beta is taken from the at noise_level * RMS, where RMS is the total variance of the observations. This norm should automatically create an image scaling that picks out low-surface brightness features and highlights.

Parameters:

  • observation (py:class:`~scarlet2.Observation`) – Observation object with weights

  • channel_map (array) – Linear mapping from channels to RGB, dimensions (3, channels)

  • minimum (float) – Minimum value to consider.

  • upper_percentile (float) – Upper percentile: Pixel values above will be saturated.

  • noise_level (float) – Factor to be multiplied to the total noise RMS to define the turnover point

  • vibrance (float) – Allowance to exceed normalization of three-channel image. Makes images more vibrant but causes slight color shifts in the highlights.

__call__(img)#

Compute Asinh normalized image

clip(im, min_value, max_value)#

Clip image between min_value and max_value

convert_to_uint8(im)#

Convert three-channel image to RGB image with uint8 dtype

get_intensity(im)#

Compute total intensity image

make_rgb_image(*im)#

Compute RGB image from three-channel image

set_rgb_max(img, vibrance=0.15)#

Set maximum value of normalized image

Parameters:

  • img (array) – Three-channel image

  • vibrance (float) – Allowance to exceed normalization of three-channel image. Makes images more vibrant but causes slight color shifts towards white in the highlights.

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