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LSST Applications g0fba68d861+2e894914a0,g1ec0fe41b4+e220e2fb2f,g1f759649c2+d3ce33c3e0,g1fd858c14a+2b9bf32e51,g35bb328faa+fcb1d3bbc8,g4d2262a081+1dc91b7776,g53246c7159+fcb1d3bbc8,g56a49b3a55+1053ce1741,g60b5630c4e+d3ce33c3e0,g67b6fd64d1+fad15079a7,g78460c75b0+2f9a1b4bcd,g786e29fd12+cf7ec2a62a,g8180f54f50+9253e245c2,g8352419a5c+fcb1d3bbc8,g8852436030+f11a5d3b0b,g89139ef638+fad15079a7,g9125e01d80+fcb1d3bbc8,g94187f82dc+d3ce33c3e0,g989de1cb63+fad15079a7,g9ccd5d7f00+44d9ee3d90,g9d31334357+d3ce33c3e0,g9f33ca652e+9a8c17f5f6,gabe3b4be73+1e0a283bba,gabf8522325+94c30d56e9,gb1101e3267+90933e15fb,gb58c049af0+f03b321e39,gb89ab40317+fad15079a7,gc0af124501+26f6120d90,gcf25f946ba+f11a5d3b0b,gd6cbbdb0b4+8d7f1baacb,gd794735e4e+4bba874dfe,gdb1c4ca869+16879ca1a6,gde0f65d7ad+0609b2c34e,ge278dab8ac+4d6e48c014,ge410e46f29+fad15079a7,gf5e32f922b+fcb1d3bbc8,gf618743f1b+dd10d22602,gf67bdafdda+fad15079a7,w.2025.17
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 |