scarlet2.plot

Plotting functions

Classes

AsinhAutomaticNorm(observation[, ...])	AsinhAutomaticNorm class
AsinhNorm(min_value, max_value, beta)	AsinhNorm class
AsinhPercentileNorm(img[, percentiles, vibrance])	AsinhPercentileNorm class
LinearNorm(minimum, maximum)	Class for linear normalization
LinearPercentileNorm(img[, percentiles])	Class for linear normalization based on percentiles
Norm()	Base class to normalize the color values of RGB images

Functions

channels_to_rgb(channels)	Get the linear mapping of multiple channels to RGB channels.
confidence(scene, observation)	The confidence of each source in scene based on the hallucination score
cut_square_box(arr, center, size)	Cut out a square box from a 2D array based on the center and size.
footprints(detect, fps)	Plot wavelet-scale detection images with overlaid footprints and peaks.
hallucination_score(scene, obs, src_num)	Calculate the hallucination score of a source in scene based on obs
hvp(f, primals, tangents)	Calculate the Hessian-vector product of a function f
hvp_grad(grad_f, primals, tangents)	Calculate the Hessian-vector product of a gradient function grad_f
hvp_rad(hvp, shape)	Approximate the diagonal of the Hessian
img_to_3channel(img[, channel_map])	Convert multi-band image cube into 3 RGB channels
img_to_rgb(img[, channel_map, norm, mask])	Convert images to normalized RGB.
log_like(morph, spectrum, data, weights)	Calculate the log-likelihood of the model given the data
neural_grad(galaxy, src)	Calculate the gradient of the neural network
observation(observation[, norm, ...])	Plot observation
scene(scene[, observation, norm, ...])	Plot all sources to recreate the scene.
sources(scene[, observation, norm, ...])	Plot all sources in scene