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LSST Applications g013ef56533+0bb79c0474,g083dd6704c+a047e97985,g199a45376c+0ba108daf9,g19c4beb06c+4eb3dd413d,g1fd858c14a+632e33a914,g210f2d0738+500288da4f,g262e1987ae+5c3595ffa8,g2825c19fe3+0dc607187f,g29ae962dfc+8c723940ef,g2cef7863aa+aef1011c0b,g35bb328faa+8c5ae1fdc5,g3fd5ace14f+e8badeebe4,g47891489e3+f459a6810c,g511e8cfd20+4122fef1c5,g53246c7159+8c5ae1fdc5,g54cd7ddccb+890c8e1e5d,g5fd55ab2c7+7caddb52a2,g64539dfbff+500288da4f,g67b6fd64d1+f459a6810c,g74acd417e5+77f7249258,g786e29fd12+668abc6043,g87389fa792+8856018cbb,g89139ef638+f459a6810c,g8d7436a09f+16e29bc969,g8ea07a8fe4+81eaaadc04,g90f42f885a+34c0557caf,g9486f8a5af+bb904a34d6,g97be763408+b03779f6b0,gbf99507273+8c5ae1fdc5,gc2a301910b+500288da4f,gca7fc764a6+f459a6810c,gce8aa8abaa+8c5ae1fdc5,gd7ef33dd92+f459a6810c,gdab6d2f7ff+77f7249258,ge410e46f29+f459a6810c,ge41e95a9f2+500288da4f,geaed405ab2+e3b4b2a692,gf9a733ac38+8c5ae1fdc5,w.2025.42
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 |