LSST Applications g044012fb7c+304891ab8a,g04a91732dc+4e1b87f259,g07dc498a13+f701f15b83,g114c6a66ad+c7887c1284,g1409bbee79+f701f15b83,g1a7e361dbc+f701f15b83,g1fd858c14a+6ebd102b59,g35bb328faa+0eb18584fe,g3bd4b5ce2c+e83bf4edc8,g4e0f332c67+976ceb6bc8,g53246c7159+0eb18584fe,g5477a8d5ce+51234355ef,g60b5630c4e+c7887c1284,g623d845a50+c7887c1284,g6f0c2978f1+98123c34b6,g71fabbc107+c7887c1284,g75b6c65c88+ce466f4385,g78460c75b0+85633614c8,g786e29fd12+02b9b86fc9,g8852436030+cfe5cf5b7b,g89139ef638+f701f15b83,g9125e01d80+0eb18584fe,g95236ca021+d4f98599f0,g974caa22f6+0eb18584fe,g989de1cb63+f701f15b83,g9f33ca652e+b4908f5dcd,gaaedd4e678+f701f15b83,gabe3b4be73+543c3c03c9,gace736f484+07e57cea59,gb1101e3267+487fd1b06d,gb58c049af0+492386d360,gc99c83e5f0+a513197d39,gcf25f946ba+cfe5cf5b7b,gd0fa69b896+babbe6e5fe,gd6cbbdb0b4+3fef02d88a,gde0f65d7ad+e8379653a2,ge278dab8ac+ae64226a64,gfba249425e+0eb18584fe,w.2025.07
LSST Data Management Base Package
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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 |