Plotting

The wcc_etc.plotting module provides ready-made diagnostic plots in both matplotlib and bokeh, plus a shared house style. Every plotting function comes in a *_mpl and a *_bokeh variant.

The house style

Apply the shared theme once per session/notebook:

import wcc_etc
wcc_etc.set_wcc_style()        # global matplotlib rcParams
wcc_etc.WCC_STYLE              # the underlying rcParams dict

The style uses STIX math/text fonts, 150 dpi, inward ticks, and faint gridlines on 1-D plots (image plots turn the grid off).

The four plots

Function pair	What it shows
`plot_image_mpl` / `plot_image_bokeh`	A single image with equal x/y scale, a `noise=` toggle, and an optional `show_saturation=` mask overlay.
`plot_image_row_mpl` / `plot_image_row_bokeh`	A three-panel row: PSF+Noise / PSF (no noise) / saturation mask, on a shared color scale.
`plot_radial_mpl` / `plot_radial_bokeh`	The azimuthally-averaged radial profile with a half-width marker.
`plot_encircled_energy_mpl` / `plot_encircled_energy_bokeh`	The encircled-energy curve (normalized to 1) with an optional `ee_target=` marker.

Two ways to call them

Each function accepts either a SimulatedImage as the first positional argument, or raw arrays via keywords (image_e=, image_clean=, saturation_mask=, pixel_scale_mas=):

from wcc_etc import plot_image_mpl, plot_radial_mpl

# From a SimulatedImage
plot_image_mpl(img, show_saturation=True)

# From raw arrays
plot_radial_mpl(image_e=arr, pixel_scale_mas=18.0, units="mas")

Convenience methods on `SimulatedImage`

The image object dispatches to the functions above, with a backend= switch:

img.plot_image(backend="mpl", show_saturation=True)
img.plot_image_row()                       # the 3-panel row
img.plot_radial(units="mas")
img.plot_encircled_energy(units="mas", ee_target=0.8)

Return values and embedding

The matplotlib variants return (fig, ax) (plus (x, y) data for the 1-D plots), so you can pass an existing ax= and compose figures.
The bokeh variants take return_="obj" | "html" | "components": use "obj" for bokeh.io.show() in a notebook, or "components" to get (script, div) for embedding in the Flask web portal.

# matplotlib: overplot two profiles on one axis
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
plot_radial_mpl(img_a, ax=ax)
plot_radial_mpl(img_b, ax=ax)

# bokeh: components for a web template
script, div = img.plot_image(backend="bokeh", return_="components")