LSST Applications g07dc498a13+5a531fccd6,g1409bbee79+5a531fccd6,g1a7e361dbc+5a531fccd6,g1fd858c14a+bae9e05889,g28da252d5a+b6acab2954,g33399d78f5+749e2df9f6,g35bb328faa+e55fef2c71,g3bd4b5ce2c+753c3426d3,g3d4cdeeb7c+495e717508,g43bc871e57+32b9ddb877,g53246c7159+e55fef2c71,g60b5630c4e+f9e43d3906,g6e5c4a0e23+f441d97430,g78460c75b0+8427c4cc8f,g786e29fd12+307f82e6af,g8534526c7b+af2545e932,g85d8d04dbe+ded3a614ca,g89139ef638+5a531fccd6,g8b49a6ea8e+f9e43d3906,g9125e01d80+e55fef2c71,g989de1cb63+5a531fccd6,g9a9baf55bd+f1bd1a7c26,g9f33ca652e+c963d5c8aa,gaaedd4e678+5a531fccd6,gabe3b4be73+9c0c3c7524,gb092a606b0+a33ed67792,gb58c049af0+28045f66fd,gc2fcbed0ba+f9e43d3906,gca43fec769+e55fef2c71,gcf25f946ba+749e2df9f6,gd6cbbdb0b4+784e334a77,gde0f65d7ad+a0ab96d407,ge278dab8ac+25667260f6,geab183fbe5+f9e43d3906,gecb8035dfe+0fa5abcb94,gefa07fa684+89734069dd,gf58bf46354+e55fef2c71,gfe7187db8c+55cd7d2043,w.2025.01
LSST Data Management Base Package
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display.py File Reference

Go to the source code of this file.

Classes

class  lsst.scarlet.lite.display.LinearPercentileNorm
 
class  lsst.scarlet.lite.display.AsinhPercentileNorm
 

Namespaces

namespace  lsst
 
namespace  lsst.scarlet
 
namespace  lsst.scarlet.lite
 
namespace  lsst.scarlet.lite.display
 

Functions

np.ndarray lsst.scarlet.lite.display.channels_to_rgb (int channels)
 
np.ndarray lsst.scarlet.lite.display.img_to_3channel (np.ndarray img, np.ndarray|None channel_map=None, float fill_value=0)
 
np.ndarray lsst.scarlet.lite.display.img_to_rgb (np.ndarray|Image img, np.ndarray|None channel_map=None, float fill_value=0, Mapping|None norm=None, np.ndarray|None mask=None)
 
matplotlib.pyplot.Figure lsst.scarlet.lite.display.show_likelihood (Blend blend, tuple[float, float]|None figsize=None, **kwargs)
 
 lsst.scarlet.lite.display._add_markers (Source src, tuple[float, float, float, float] extent, matplotlib.pyplot.Axes ax, bool add_markers, bool add_boxes, dict marker_kwargs, dict box_kwargs)
 
 lsst.scarlet.lite.display.show_observation (Observation observation, Mapping|None norm=None, np.ndarray|None channel_map=None, Sequence|None centers=None, str|None psf_scaling=None, tuple[float, float]|None figsize=None)
 
matplotlib.pyplot.Figure lsst.scarlet.lite.display.show_scene (Blend blend, Mapping|None norm=None, np.ndarray|None channel_map=None, bool show_model=True, bool show_observed=False, bool show_rendered=False, bool show_residual=False, bool add_labels=True, bool add_boxes=False, tuple[float, float]|None figsize=None, bool linear=True, bool use_flux=False, dict|None box_kwargs=None)
 
tuple[int, int, int, int] lsst.scarlet.lite.display.get_extent (Box bbox)
 
matplotlib.pyplot.Figure lsst.scarlet.lite.display.show_sources (Blend blend, list[Source]|None sources=None, Mapping|None norm=None, np.ndarray|None channel_map=None, bool show_model=True, bool show_observed=False, bool show_rendered=False, bool show_spectrum=True, tuple[float, float]|None figsize=None, bool model_mask=True, bool add_markers=True, bool add_boxes=False, bool use_flux=False)
 
matplotlib.pyplot.Figure lsst.scarlet.lite.display.compare_spectra (bool use_flux=True, bool use_template=True, **list[Source] all_sources)
 

Variables

float lsst.scarlet.lite.display.panel_size = 4.0