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LSST Applications g00d0e8bbd7+edbf708997,g03191d30f7+6b31559d11,g118115db7c+ac820e85d2,g199a45376c+5137f08352,g1fd858c14a+90100aa1a7,g262e1987ae+64df5f6984,g29ae962dfc+1eb4aece83,g2cef7863aa+73c82f25e4,g3541666cd7+1e37cdad5c,g35bb328faa+edbf708997,g3fd5ace14f+fb4e2866cc,g47891489e3+19fcc35de2,g53246c7159+edbf708997,g5b326b94bb+d622351b67,g64539dfbff+dfe1dff262,g67b6fd64d1+19fcc35de2,g74acd417e5+cfdc02aca8,g786e29fd12+af89c03590,g7aefaa3e3d+dc1a598170,g87389fa792+a4172ec7da,g88cb488625+60ba2c3075,g89139ef638+19fcc35de2,g8d4809ba88+dfe1dff262,g8d7436a09f+db94b797be,g8ea07a8fe4+79658f16ab,g90f42f885a+6577634e1f,g9722cb1a7f+d8f85438e7,g98df359435+7fdd888faa,ga2180abaac+edbf708997,ga9e74d7ce9+128cc68277,gbf99507273+edbf708997,gca7fc764a6+19fcc35de2,gd7ef33dd92+19fcc35de2,gdab6d2f7ff+cfdc02aca8,gdbb4c4dda9+dfe1dff262,ge410e46f29+19fcc35de2,ge41e95a9f2+dfe1dff262,geaed405ab2+062dfc8cdc,w.2025.46
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 |