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LSST Applications g0fba68d861+e3d3968345,g123d84c11c+8c5ae1fdc5,g1ec0fe41b4+6d748b5e26,g1fd858c14a+9065d10d75,g2d69826f85+7a6d91523d,g3533f9d6cb+7a6d91523d,g35bb328faa+8c5ae1fdc5,g365e2b32db+66dbf0551a,g4465070510+1e5fc721e3,g53246c7159+8c5ae1fdc5,g5e1b9f9e8c+6f557685ca,g60b5630c4e+7a6d91523d,g663da51e9b+c06a6e841e,g6735e52a0d+a4ee9a8f61,g67b6fd64d1+4c6ad10a27,g78460c75b0+7e33a9eb6d,g786e29fd12+668abc6043,g8352419a5c+8c5ae1fdc5,g8852436030+b5e471a2b0,g89139ef638+4c6ad10a27,g989de1cb63+4c6ad10a27,g9f33ca652e+61210f6abb,ga1e959baac+5fbc491aed,ga2f891cd6c+7a6d91523d,gabe3b4be73+8856018cbb,gabf8522325+1beb3916ec,gac2eed3f23+4c6ad10a27,gb1101e3267+521a05703f,gb89ab40317+4c6ad10a27,gcf25f946ba+b5e471a2b0,gd107969129+0eec2b61af,gd6cbbdb0b4+1b25e5bbbb,gde0f65d7ad+b2d31fd042,ge278dab8ac+ddfffb0679,ge410e46f29+4c6ad10a27,gf30d85a44d+13a9557852,gf5e32f922b+8c5ae1fdc5,gff02db199a+fd0635536e,w.2025.26
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 |