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 The mapping created here assumes the the channels are ordered in wavelength direction, starting with the shortest wavelength.
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.
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)	Gradient of neural_grad with respect to positional argument(s) 0.
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